Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.

Introduction

Immunotherapy encompasses a wide range of strategies that harness components of the immune system, such as antibodies, immune cells, and cytokines, to treat cancers and autoimmune conditions. It also includes immunomodulation through vaccines aimed at preventing and managing infectious and allergic diseases( 1 ). In this review, “precision immunotherapy” refers to the biomarker-informed selection and dynamic adaptation of immune-based interventions for an individual patient, integrating tumor/immune characteristics, microenvironmental context, and longitudinal monitoring of response and toxicity( 2 ). Accordingly, we cover representative AI approaches ranging from classical machine learning (e.g., regularized regression, random forests, support vector machines) to deep learning architectures—including convolutional neural networks (CNNs) for radiology and whole-slide pathology, and transformer-based models/protein language models for sequence- and structure-informed prediction—applied across multi-omics (including single-cell RNA-seq), immunopeptidomics, medical imaging, digital pathology, and real-world clinical data such as electronic health records (EHRs) and wearable-derived signals.

Cancer remains a major global health concern, causing nearly 10 million deaths each year and ranking as the second leading cause of mortality worldwide. This burden is influenced by aging populations, lifestyle changes, and exposure to risk factors such as tobacco use, obesity, and physical inactivity( 3 ). Conventional cancer treatments, including surgery, chemotherapy, and radiotherapy, remain the foundation of care but face significant limitations. Chemotherapy, although effective against rapidly dividing tumor cells, lacks specificity and harms healthy tissues, leading to adverse effects and a reduced quality of life. Resistance to chemotherapeutic agents, whether intrinsic or acquired, further compromises long-term outcomes. Radiotherapy provides greater precision but can still damage adjacent tissues, causing complications such as skin injury and fatigue. These drawbacks underscore the urgent need for more innovative, targeted, and less toxic therapeutic alternatives( 4 , 5 ).

Cancer immunotherapy, which activates the host immune system to recognize and eliminate malignant cells, has emerged as one of the most promising therapeutic strategies.

Tumors frequently evade immune surveillance by exploiting immune checkpoint pathways, which suppress T-cell activation. Clinically relevant checkpoint molecules include programmed death-1 (PD-1), its ligand PD-L1, and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4)( 6 , 7 ). Significant clinical advances in immunotherapy include immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR) T cell therapies, therapeutic cancer vaccines, and adoptive cell transfer techniques( 8 , 9 ). Conceptually, AI supports immunotherapy along two complementary axes: (i) target discovery and therapeutic engineering (e.g., antigen/neoantigen prioritization, CAR construct optimization, and antibody design), and (ii) clinical decision support (e.g., integrating multimodal patient data to predict ICI response/toxicity, guide treatment selection and sequencing, and enable monitoring and adaptive management)( 10 ).

AI is increasingly integral to advancing cancer immunotherapy by refining target discovery, predicting treatment outcomes, and enabling personalized therapeutic strategies ( 11 ). For instance, researchers at the Cleveland Clinic and IBM demonstrated that AI models simulating peptide antigen dynamics can identify viable immunotherapy targets more efficiently than conventional trial-and-error approaches, particularly by capturing antigen structural changes in influencing immune recognition( 12 ). AI-driven platforms now integrate diverse datasets, including genomic, proteomic, and imaging data, to predict patient responses to ICIs and optimize combination strategies( 13 ). These approaches improve diagnostic accuracy by identifying biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), and facilitate the discovery of novel therapeutic targets, like neoantigens( 14 , 15 ). Such tools support the design of personalized treatment regimens, enhancing efficacy while reducing adverse effects. Nonetheless, challenges persist in data standardization, model interpretation, and clinical integration, which must be addressed to fully realize AI’s transformative potential in precision oncology( 16 ).

AI also plays a critical role in the diagnosis and management of autoimmune diseases through precise risk assessment and individualized therapeutic planning( 17 ). Autoimmune disorders result from the immune system’s failure to distinguish between self and non-self, leading to chronic inflammation, tissue damage, and diverse clinical manifestations( 18 ). These conditions can affect nearly any organ system. Notable examples include rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and type 1 diabetes mellitus (T1DM), all of which share immune dysregulation as a core mechanism despite differing clinical features( 18 , 19 ).

Cutting-edge AI algorithms are increasingly applied to analyze high-dimensional genetic and molecular data, uncovering new gene-disease associations and immune pathways implicated in autoimmune pathogenesis. For example, AI has improved risk prediction and treatment stratification in SLE and RA by integrating omics data with ML models( 20 ). These tools facilitate early diagnosis, enable patient stratification based on molecular signatures, and predict therapeutic efficacy, thus supporting precision treatment strategies( 21 ).

Drug repurposing is also being explored in immune-mediated diseases; for example, metformin has been evaluated as adjunct therapy in T1DM, with outcomes that can vary by cohort and clinical context( 22 , 23 ).While challenges such as data heterogeneity and algorithmic bias remain, AI continues to drive innovation in precision medicine for autoimmune disorders, ultimately improving clinical outcomes and quality of life( 20 , 21 , 24 ). This review examines recent advances in AI applications in immunotherapy, with a focus on their growing role in cancer treatment and autoimmune diseases management.

Statement of significance .

Heading Summary Problem or Issue Immunotherapy has transformed treatments for cancer and autoimmune diseases, yet major challenges remain in predicting therapeutic response, optimizing drug delivery, and ensuring patient safety, largely due to biological heterogeneity and the complexity of integrating multi-source biomedical data. What is Already Known AI has been applied separately in oncology and autoimmune disorders, showing promise in biomarker discovery, patient stratification, drug design, and outcome prediction. However, most reviews address one disease area in isolation, limiting cross-disciplinary insights. What This Paper Adds This work bridges oncology and autoimmune immunotherapy, offering a translational and pharmaceutical informatics perspective on AI-driven solutions — including explainable AI, federated learning, and digital twins — to address shared therapeutic challenges and accelerate precision medicine. Who Would Benefit from the New Knowledge in This Paper Biomedical informatics researchers, clinical immunologists, pharmaceutical scientists, and healthcare decision-makers involved in developing AI-enabled immunotherapies or optimizing targeted drug delivery for complex immune-mediated diseases.

AI advances in immunotherapy

AI-powered imaging biomarkers increasingly enable non-invasive tumor characterization and evaluation of the tumor microenvironment (TME), thereby enhancing patient selection for immunotherapy( 25 , 26 ). For instance, in non-small cell lung cancer (NSCLC) and melanoma, AI-derived radiomic features extracted from pre-treatment contrast-enhanced computed tomography (CT) scans have distinguished responders from non-responders. Radiographic heterogeneity- including irregular tumor borders and variable internal density-has been associated with favorable response to immune checkpoint inhibitors (ICIs) in multiple cohorts ( 25 , 27 , 28 ). In digital pathology, AI has revolutionized histological image analysis by enabling automated, high-throughput examination of tumor tissue samples. Generative Adversarial Networks (GANs), have shown remarkable ability to synthesize high-resolution histological images and capture latent image features through unsupervised learning. These advances have improved nuclear segmentation, detection, and domain adaptation, ultimately deepening our understanding of immune cell distribution and tumor architecture within the TME( 29 , 30 ). The conceptual framework for applying AI in immunotherapy, including data acquisition, preprocessing, model development, and translational applications, is summarized in Fig. 1 .

Fig. 1 Schematic representation of AI applications in immunotherapy. AI workflows typically begin with the integration of diverse data sources, including genomics, proteomics, transcriptomics, EHRs, and medical imaging. These are subjected to preprocessing to create structured datasets suitable for ML or DL model development. The resulting predictive models are applied to clinical scenarios involving cancer and autoimmune diseases, supporting key tasks including treatment response prediction, biomarker identification, and patient stratification. The feedback loop from clinical deployment informs model refinement and guides ongoing development— Abbreviations : EHR , electronic health record; ML , machine learning; DL , deep learning

AI has also played a key role in predicting TMB, a pivotal biomarker associated with responsiveness to immunotherapy. ML algorithms trained to classify TMB levels have enabled more accurate stratification of patients into groups with distinct survival outcomes following ICI treatment( 31 ). Similarly, radiomic signatures derived from CT imaging have been used to characterize immune phenotypes, distinguishing tumor-inflamed and tumor-desert subtypes. High signature scores, reflecting elevated CD8 + T-cell infiltration, correlate with improved objective response rates and survival( 32 , 33 ). In addition, AI has contributed significantly to post-treatment evaluation. Radiomic-based models comparing baseline and on-treatment imaging, can identify patients with poor therapeutic sensitivity and shortened overall survival. These models quantify dynamic tumor changes, such as alteration in volume, boundary invasion, and spatial heterogeneity, providing early indicators of resistance ( 31 , 34 ).

Clinical translation is progressing. Although most radiomic signatures for ICI response remain investigational, deep-learning radiomic biomarkers have undergone large, multi-institution validation in real-world cohorts and have also been evaluated using prospective clinical-trial datasets in advanced NSCLC, illustrating movement toward clinically testable decision support for immunotherapy selection( 35 ). In parallel, peer-reviewed regulatory analyses indicate that regulators (including the FDA) have authorized a growing number of AI-enabled medical imaging devices and are refining oversight frameworks for AI software as a medical device, emphasizing clinical validation, generalizability across sites, bias mitigation, and lifecycle/post-deployment monitoring( 36 ). Despite these advances, challenges remain in integrating AI into routine immunotherapy practice. Variability in data quality, limited model interpretability, and the need for large-scale, multicenter validation hinder clinical translation. Addressing these barriers will be essential. Future research should emphasize advanced frameworks such as reinforcement learning for adaptive treatment strategies and federated learning to support secure, privacy-preserving collaboration across institutions( 29 , 37 ).

AI in cancer immunotherapy

AI is redefining the landscape of cancer immunotherapy by processing large-scale, multidimensional biological datasets and discover predictive biomarkers that inform patient-specific responses to treatments, such as immune checkpoint inhibitors (ICIs)( 38 ). One key advancement is the development of platforms such as SCORPIO (Standard Clinical and laboratory featuRes for Prognostication of Immunotherapy Outcomes), a machine-learning model that predicts ICI benefit using widely available clinical variables, including routine blood tests (e.g., complete blood count and standard chemistry panels) and basic patient characteristics. In a large multi-cohort study including 9,745 ICI-treated patients across 21 cancer types, SCORPIO was trained on an internal cohort and validated across independent datasets, showing AUCs in the ~ 0.7–0.76 range for survival/clinical benefit prediction( 39 ). Additionally, AI-based integrative models that combine genomic, proteomic, and imaging data have substantially improved the accuracy of therapeutic outcome predictions. These models not only facilitate the optimization of treatment strategies but also enable the identification of novel immunogenic targets, such as neoantigens. These advancements help address the limitations of conventional biomarkers like PD-L1, which often fail to capture tumor heterogeneity or the dynamics of the immune microenvironment( 16 , 40 ). AI has made significant progress in refining predictive biomarker discovery, which is pivotal in enhancing the personalization of cancer care. Furthermore, AI is instrumental in individualizing immunotherapy by evaluating patient response profiles and enabling real-time adjustments to treatment. ML algorithms, particularly those trained on histopathological images and molecular features, have proven effective in predicting immunotherapy responses across various solid tumors( 41 , 42 ). For instance, models developed using mass spectrometry-based human leukocyte antigen (HLA) datasets have greatly enhanced the identification of tumor-specific neoantigens, thereby improving immune targeting and overall therapeutic efficacy. Moreover, AI systems enable continuous monitoring of patient responses throughout the treatment course, offering clinicians actionable insights that support early intervention and adaptive therapy planning( 33 , 43 ). Despite its transformative potential, the full integration of AI into clinical oncology faces challenges that must be addressed for widespread adoption. These include issues related to data privacy, algorithmic transparency, and the interpretability of complex ML models. As AI models become more advanced, they will require robust regulatory frameworks and transparent standards to ensure their ethical use in clinical practice. Overcoming these barriers will be essential for the successful implementation of AI in routine oncological care. As these challenges are progressively addressed, AI is poised to play an increasingly central role in modern immuno-oncology, supporting precision and personalized cancer treatment( 44 ). An overview of key AI applications in cancer immunotherapy is presented in Table 1.

Table 1 AI Applications in Cancer Immunotherapy AI/ML Model or Algorithm Type Cancer Type(s) Data Modalities Used AI Application Purpose Performance Metrics Clinical or Translational Impact References Deep Learning (CNNs, RNNs, Transformers) Non-small cell lung cancer(NSCLC) Radiomics (CT), histopathology, genomics PD-L1 prediction, TMB prediction, and ICIs response classification AUC 0.80–0.95, Sensitivity >85% Enhanced patient stratification, non-invasive biomarker analysis ( 51 , 52 , 107 , 108 ) melanoma Radiomics, Whole-slide imaging, Genomics Overall survival prediction, neoantigen discovery, and immune phenotype classification Accuracy >90%, AUC 0.92 Improved immunotherapy targeting and survival stratification ( 56 , 65 , 135 ) colorectal cancer Histopathology, Clinical data, Genomics MSI/dMMR status prediction, TMB prediction Clinical-grade accuracy, AUC >0.85 Supports FDA biomarker-based therapies ( 124 , 125 , 130 ) Head and Neck Squamous Cell Carcinoma Transcriptomics, Genomics Neoantigen prioritization, immune receptor profiling High precision in immune target selection Precision targeting of immune checkpoint blockade ( 307 ) Bladder Cancer Histopathology, Clinical Data Immune landscape profiling, TILs prediction Accuracy >88% Better immune characterization for ICIs ( 308 , 309 ) Random Forest/Gradient Boosting Machines Melanoma Radiomics (CT), clinical data, and immune profiling Predict ICI response; classify tumor immune phenotypes AUC: 0.85–0.90; Accuracy: ~85% Improves ICI eligibility prediction and patient stratification ( 57 ) Breast Cancer Genomics, proteomics, imaging (radiology) Drug response prediction; immune subtype classification Accuracy: ~88%; AUC: 0.91 Optimizes biologics and targeted therapy selection ( 310 ) Gastric Cancer Clinical variables, radiological data, multi-omics Biomarker discovery, survival outcome modeling AUC ~0.86, precision ~83% Enhances prognosis and therapy planning ( 311 – 314 ) Support Vector Machines (SVMs) NSCLC Radiomics (CT/MRI), histopathology, clinical parameters Immune phenotype classification, TME analysis, biomarker prediction Sensitivity >85%, Specificity >80% Improves immunotherapy stratification; guides biomarker validation ( 315 , 318 ) Reinforcement Learning CAR-T applicable cancers (hematologic malignancies, solid tumors) Clinical data, CAR cell characteristics, treatment parameters Optimization of CAR infusion dosing and scheduling protocols Early-stage development showing promise for therapy optimization Potential for improved CAR-T production efficiency and personalized treatment adaptation ( 65 ) Deep Neural Networks (DNNs) Melanoma Peptide sequences, Protein language models (ProtTrans), Multi-window CNN Neoantigen prediction without requiring specific MHC allele information AUC: ~0.93; MCC: ~0.53; Accuracy: ~92% Facilitates accurate neoantigen identification for personalized cancer vaccines and immunotherapies ( 319 ) NSCLC Genomics Clinical Data, PET/CT imaging Gene mutation prediction (EGFR, KRAS, ALK) AUC: 0.94 Enables precise targeted therapies and improved mutation-driven treatment planning ( 320 ) Transformers (BERT, BioBERT) Head and Neck Cancer, Melanoma Genomic sequences, transcriptomics Neoantigen prioritization, immune repertoire profiling High precision in antigen prediction Enhanced immune receptor mapping; improved selection of immunotherapy targets ( 321 ) Multi-omics Integration Models Lung, Breast, Colorectal Cancers Genomics, transcriptomics, proteomics, metabolomics, clinical data Integrated biomarker discovery, response prediction, and TME characterization AUC >0.90 Comprehensive tumor profiling, precision immunotherapy, and identification of novel therapeutic targets ( 322 ) Graph Neural Networks (GNNs) NSCLC PET/CT radiomics and transcriptomic data Predict lymph node metastasis; integrate genomic and imaging features AUC = 0.85 Supports prognosis modeling; enhances multimodal integration for clinical risk assessment ( 323 ) Convolutional Autoencoders Melanoma Dermoscopic images Skin lesion classification via dimensionality reduction and deep learning Accuracy ~98–99% Enhances early diagnosis and classification of melanoma and other skin cancers ( 324 ) Transfer Learning Rare Cancers, Heterogeneous Tumors Imaging Adapt models to small datasets; improve classification and reduce training time Accuracy up to 94.2% with small datasets Enables AI application to rare cancers; improves generalizability with limited data ( 325 ) Attention-based Multi-task DL NSCLC Imaging (CT) Subtype classification and staging; highlights discriminative features AUC ~0.96 Improves interpretability and supports clinical decision-making ( 326 ) Explainable AI (XAI) Methods All Cancer Types Multi-omics, imaging, and clinical data Model interpretability, biomarker importance elucidation Improved clinical trust; diagnostic accuracy >90% Enhances clinician acceptance; reveals novel biomarker roles; supports precision treatment ( 327 – 329 ) Capsule Networks NSCLC (Lung), Colon Histopathology images Spatial and morphological feature extraction Accuracy ~92–99% Captures hierarchical features; improves diagnostic performance; aids in tumor grading ( 330 – 332 ) Hybrid Models (ML + DL Ensembles) Lung, Colon, Breast Cancers Histopathological images, clinical records, multimodal data Tumor subtype classification, biomarker discovery, patient stratification Accuracy up to 98.9%, AUC ~0.95 Integrates imaging and clinical features; enhances diagnostic accuracy; supports personalized therapy decisions ( 333 – 336 )

AI in predicting response to immune checkpoint inhibitors

ICIs are among the most thoroughly studied and widely utilized immunotherapeutic agents in oncology. They have received FDA approval for treating a variety of cancers, demonstrating significant efficacy in clinical trials( 45 ). In normal physiology, immune checkpoints regulate the immune system by balancing activation and suppression, maintaining immune homeostasis and preventing autoimmunity or excessive inflammation( 46 ). However, cancer cells frequently exploit these regulatory pathways to evade immune surveillance, thereby fostering an immunosuppressive TME that hinders effective immune responses( 47 ). AI plays a crucial role in predicting by learning outcomes-associated patterns from large, heterogenous datasets spanning genomics, transcriptomics, epigenomics, radiomics, and digital pathology, enabling the construction of composite predictive signatures that reflect both tumor-intrinsic features and the immune contexture( 33 , 48 ).

PD-L1 remains a critical component of response assessment, and AI-based pipelines—particularly deep learning applied to whole-slide images and ML-based radiomic frameworks—have improved the consistency and scalability of PD-L1-related evaluation across tumor types( 49 , 50 ). In NSCLC, AI-assisted whole-slide image analysis has demonstrated improved concordance among pathologists, reaching 90.2% agreement, surpassing traditional manual evaluations. Moreover, multimodal AI systems that integrate pathological and CT-derived features have achieved a validation AUC of 0.80 for predicting response to ICIs, illustrating the value of combining complementary data sources for outcome prediction( 51 , 52 ).

Beyond PD-L1, AI models incorporate additional determinants of ICI benefit, including HLA-related features and TMB. ML frameworks that combine transcriptomic, radiogenomic, and digital pathology data have can improve predictive performance by capturing interactions between antigen presentation capacity, tumor heterogeneity and immune infiltration ( 53 , 54 ). For instances, ML analyses, have linked loss of heterozygosity in HLA genes and intra-tumoral heterogeneity with ICI efficacy in NSCLC( 55 , 56 ). Epigenomic signals, including DNA methylation patterns, have also been explored as predictors of immunotherapy response in solid tumors. In parallel, radiomic features reflecting tumor volume, spatial heterogeneity, and edge characteristics, have been associated with clinical outcomes, in melanoma, such features have correlated strongly with overall survival in ICI-treated patients, with AUC values reaching 0.92, supporting non-invasive prediction strategies( 56 , 57 ).

The TME is a complex and dynamic ecosystem composed of diverse immune and stromal cell populations, soluble mediators, and extracellular matrix components that collectively shape immunotherapy responsiveness( 58 ). AI enables systematic characterization of this environment and supports response prediction using immune-related variables such as tumor-infiltrating lymphocytes, systemic inflammatory indices (e.g., neutrophil-to-lymphocyte ratio), and tumor stemness-associated patterns ( 59 , 60 ). These computational approaches facilitate immune-phenotype classification and help contextualize mechanisms such as T-cell exhaustion and immune evasion. Additionally, integrating emerging platforms (e.g., Raman spectroscopy) with ML has shown promise for assessing treatment effects and refining response prediction in selected settings( 61 , 62 ). A schematic overview of the AI-powered workflow for ICI biomarker prediction and treatment optimization is presented in Fig. 2 .

Fig. 2 AI-powered framework for predicting immune checkpoint inhibitor (ICI) response and supporting precision immunotherapy. Diverse data types—including immune biomarker profiles, genomic and epigenomic features, radiomic signatures, CT scans, and digital pathology slides—are integrated into ML and DL models. These models facilitate the prediction of PD-L1 expression, the discovery of novel biomarkers, and the characterization of tumor microenvironment (TME) features. Clinical outcomes derived from these analyses include improved PD-L1-based decision-making, enhanced accuracy in forecasting ICI effectiveness, and optimized treatment strategies through immune phenotype classification and T-cell exhaustion profiling. This pipeline supports the broader goal of precision oncology by enabling tailored and timely interventions in immunotherapy

Enhancing CAR-T cell therapy

CAR-T cell therapy is a revolutionary approach that utilizes genetically engineered immune cells designed to recognize and destroy cancer cells. CAR-T cell therapies have achieved remarkable clinical success, particularly in treating hematologic cancers such as B-cell acute lymphoblastic leukemia (ALL), non-Hodgkin B-cell lymphomas, and multiple myeloma, resulting in regulatory approval of several products worldwide( 63 ). However, despite these successes, challenges such as complex manufacturing processes, safety concerns, the difficulty of identifying precise tumor targets, and the regulation of CAR-T cell differentiation and persistence remain substantial barriers to wider adoption( 64 ). AI is transforming CAR-T cell therapy by optimizing each stage, from target discovery to clinical decision-making. By integrating ML and DL, AI addresses manufacturing challenges, enhances therapeutic efficacy, ensures safety, and facilitates personalized treatment strategies. Key applications include optimizing CAR construct designs using structural prediction tools, such as AlphaFold, streamlining manufacturing through predictive algorithms, and identifying biomarkers that predict clinical outcomes and adverse events, such as cytokine release syndrome (CRS)( 64 , 65 ). Importantly, AlphaFold was trained primarily on experimentally determined structures of naturally occurring proteins( 66 ); thus, when applied to engineered CAR domains, its outputs should be interpreted as hypothesis-generating structural models that require experimental validation rather than as CAR-specific ground truth( 64 ).These advancements aim to extend CAR-T therapy beyond hematologic malignancies into solid tumors and increase accessibility by lowering production costs( 67 ).

AI plays a vital role in antigen target selection and CAR design by leveraging single-cell RNA sequencing data to identify tumor-specific markers while minimizing off-target effects. For instance, deep generative models have identified myeloid cell targets such as CD86 and CSF1R for acute myeloid leukemia( 59 , 64 ). Structural prediction tools optimize the stability and signaling domains of CAR proteins, utilizing ML models trained on thousands of synthetic costimulatory motifs to enhance T-cell activation and longevity. Concurrently, AI-assisted CRISPR-Cas9 genome editing improves precision and reduces off-target mutations, enabling the creation of CAR-T cells tailored for the challenging microenvironment of solid tumors( 68 , 69 ). Manufacturing optimization constitutes another critical frontier, where AI predicts ideal culture conditions and monitors cell proliferation via real-time “soft sensors.” Initiatives like AIDPATH utilize digital twin simulations of bioreactor processes, while scheduling algorithms coordinate parallel manufacturing cycles for multiple patients. ML models analyzing multi-omics data from CyTOF and single-cell RNA sequencing predict optimal CD4/CD8 ratios and memory cell proportions in final products, potentially reducing manufacturing failures by up to 30%. These innovations aim to standardize production while preserving flexibility for patient-specific customization( 63 , 70 , 71 ).

In clinical applications, AI enhances the prediction of treatment responses and the identification of adverse events. Multivariate logistic regression models incorporating lactate dehydrogenase levels and proportions of CAR + regulatory T cells achieve approximately 85% accuracy in forecasting six-month clinical outcomes( 72 , 73 ). For toxicity management, ML algorithms analyzing cytokine profiles, including interferon-gamma (IFN-γ) and macrophage inflammatory protein-1 alpha (MIP-1α), demonstrate over 85% sensitivity in predicting severe CRS. Additionally, models trained on PET/CT imaging data facilitate the early detection of relapse. Wearable AI systems enable the remote monitoring of physiological parameters, allowing for timely intervention in cases of side effects such as neurotoxicity( 74 ). As these technologies mature, they hold promise to transform CAR-T therapy into a broadly accessible precision oncology modality capable of treating diverse cancer types with improved safety and efficacy profiles( 75 ).

AI for neoantigen prediction

Neoantigens, the tumor-specific antigens generated by somatic mutations, represent a promising frontier in cancer immunotherapy. These antigens are expressed exclusively on cancer cells, making them ideal targets for immune-based therapies. However, only a fraction of these neoantigens elicit effective immune responses, necessitating accurate identification of the most immunogenic candidates( 76 ).

AI is a vital tool in neoantigen prediction by addressing the limitations of traditional experimental methods through modeling peptide presentation by the major histocompatibility complex (MHC) and downstream T-cell receptor (TCR) recognition of the resulting peptide–MHC (pMHC) complexes ( 76 , 77 ). Importantly, high MHC binding affinity mainly reflects antigen presentation likelihood and does not necessarily equate to true immunogenicity, which also requires productive TCR recognition and functional T-cell activation( 78 ). AI-based strategies are revolutionizing neoantigen discovery by integrating multi-omics datasets to identify high-confidence targets. For example, platforms like EasyFuse utilize transcriptomic data to detect cancer-related gene fusions. In contrast, NeoDisc combines genomic, transcriptomic, and proteomic information to pinpoint immunogenic neoantigens more effectively than conventional methods( 79 ). By improving neoantigen prediction and validation, AI accelerates the translation of precision immunotherapies into clinical usage, offering new treatment opportunities for patients with heterogeneous or treatment-resistant cancers( 80 , 81 ).

MHC-peptide binding prediction models are generally divided into allele-specific and pan-specific categories. Allele-specific models are trained on peptides corresponding to particular MHC alleles, while pan-specific models predict peptide binding across multiple alleles. These models typically encode MHC and peptide sequences into fixed-length vectors using methods such as one-hot encoding or embeddings( 77 , 82 ). Deep neural networks (DNNs) and convolutional neural networks (CNNs) are frequently employed to capture intricate patterns in peptide binding data( 83 ). Notable predictors include NetMHCpan, MHCflurry, and HLAthena, which have enhanced prediction accuracy by incorporating ligand data derived from mass spectrometry( 84 – 86 ). Although substantial progress has been made in predicting MHC class I (MHC-I) binding, predicting MHC class II (MHC-II) remains challenging due to the heterogeneity in peptide length and binding motifs, compounded by the polymorphic nature of MHC-II molecules( 78 , 82 ). However, incorporating MS-derived ligand data has improved the performance of MHC-II predictors, such as CAPTAn and MARIA( 87 , 88 ). Additionally, NetMHCIIpan has increased its predictive accuracy by deconvoluting MS-eluted ligand (MS-EL) data and expanding its training dataset to cover a broader range of HLA-II specificities( 89 , 90 ). Continued research is needed further to enhance the performance of MHC-II binding prediction models.

Forecasting the recognition of pMHC complexes by TCRs is particularly complex due to the diversity of TCR rearrangements and the spatial complexity of TCR-pMHC interactions( 91 , 92 ). Experimental techniques such as multimeric pMHC assays and high-throughput library screens have been developed to study pMHC-TCR binding. Technologies, including lentiviral transfer assays and synthetic peptide display libraries, have expanded the capacity to identify paired pMHC-TCR interactions( 93 , 94 ). Despite these advances, accurately predicting neoantigens based on pMHC-TCR binding remains a challenging area that requires further methodological improvements. The identification of tumor antigens and the development of cancer vaccines constitute cutting-edge immunotherapeutic strategies. Neoantigens, derived from somatic mutations unique to individual patients’ tumors, are ideal targets for T-cell-based therapies because they are absent in normal tissues and evade central immune tolerance, allowing for specific recognition by both CD4 + and CD8 + T-cells. The personalized immunotherapy approach leverages AI to model immune interactions, optimize vaccine design, and efficiently select promising neoantigen candidates( 95 ).

Accordingly, AI-guided mRNA and peptide vaccines have already progressed to phase I clinical trials in various cancers, illustrating AI’s role in accelerating therapeutic development( 96 , 97 ). Notably, computational/AI-supported neoantigen ranking is already being translated into personalized neoantigen vaccine trials( 98 ). For example, BioNTech’s individualized mRNA neoantigen vaccine autogene cevumeran has been evaluated as adjuvant therapy in resected pancreatic ductal adenocarcinoma, including in combination with checkpoint blockade, demonstrating feasibility and induction of neoantigen-specific T-cell responses in a subset of patients( 99 ). In parallel, Moderna’s individualized neoantigen therapy mRNA-4359 has been tested clinically in combination regimens (e.g., with pembrolizumab), as evaluated in resected melanoma, providing a concrete example of personalized neoantigen vaccine development in trials( 96 ). Adjuvants further enhance vaccine efficacy by amplifying immune responses, and AI facilitates their design through virtual screening, property prediction, and drug repurposing efforts. For example, AI has identified CXCL12 inhibitors with potential as potent adjuvants( 100 , 101 ). By integrating genomics, bioinformatics, and machine learning, these innovations enable the precise selection of neoantigens and the formulation of vaccines, offering tailored solutions for tumors resistant to conventional therapies while addressing challenges such as immune suppression and tumor heterogeneity( 102 ).

AI for direct prediction of immunotherapy responses

AI in cancer immunotherapy enables the detection of subtle phenotypic differences and generates detailed data that augment human assessment ( 103 ). AI algorithms excel in processing complex medical information, including imaging and large-scale datasets, to accurately and consistently forecast patient responses to ICB therapies( 104 ). This capability supports the implementation of personalized treatment plans, ultimately improving clinical outcomes for cancer patients. In the realm of medical imaging, AI enhances radiological workflows by integrating high-dimensional data from modalities such as CT and magnetic resonance imaging (MRI)( 105 ). AI-powered radiomic models non-invasively analyze tumor morphology and TME features, offering insights that outperform traditional imaging interpretation( 106 ). These models have been particularly effective in predicting immunotherapy responses in cancers such as NSCLC, melanoma, and esophageal carcinoma. Radiomic biomarkers identified by AI also show correlations with genomic indicators, such as TMB, refining response prediction accuracy( 107 , 108 ).

Histopathology, the gold standard for cancer diagnosis, has similarly been transformed by AI. ML and DL applied to whole-slide histological images enable the precise identification and classification of cellular structures and protein expression patterns( 109 , 110 ). These AI-enhanced histopathological analyses enhance the prediction of immunotherapy responses by identifying abnormal morphological features and immune-related molecular markers within the tumor microenvironment. Such techniques facilitate more efficient patient selection for immunotherapy, potentially extending benefits to a broader patient population( 111 , 112 ). Figure 3 summarizes the major AI-integrated data domains, including medical imaging, histopathology, and immune signatures, used to predict immunotherapy outcomes directly. Beyond imaging and pathology, AI integrates multi-omics data to construct comprehensive predictive models. These incorporate a variety of biomarkers such as PD-L1 expression, TMB, tumor-infiltrating lymphocytes (TILs), and specific genetic mutations, thereby enhancing the reliability of immunotherapy response predictions. The integration of diverse data types enables a more nuanced characterization of tumors, advancing precision medicine and individualized treatment planning( 113 , 114 ).

Fig. 3 Core data modalities integrated by AI for direct prediction of immunotherapy response. AI models utilize multimodal sources including ( 1 ) medical imaging (e.g., CT, MRI, microscopy) for radiomic feature extraction ( 2 ), histopathological analysis of whole-slide tissue sections to identify immune infiltration and morphological markers, and ( 3 ) immune profiling using genomic assays, flow cytometry, and immune cell panels. These datasets enable AI to detect clinically relevant tumor and immune signatures, enhancing the precision of response prediction in cancer immunotherapy

Recent innovations include AI platforms like SCORPIO, which leverage routine clinical data, i.e., blood laboratory results and patient demographics, to predict responses to ICIs. SCORPIO surpasses current FDA-approved biomarkers in predictive performance with a faster, cost-effective, and widely accessible tool for guiding immunotherapy decisions. These predictive capabilities hold promise for improving treatment equity and reducing healthcare costs globally( 39 , 115 ). Overall, AI’s contributions to cancer immunotherapy encompass detailed phenotypic profiling, advanced image analysis, histopathological interpretation, and the integration of multi-omics datasets, collectively enhancing the therapeutic response prediction, optimizing patient stratification, and supporting personalized immunotherapy to ensure precision oncology( 16 ).

Predictive biomarkers and immunotherapy

AI models, particularly those employing ML and DL techniques, have demonstrated robust discriminative performance (e.g., AUC and sensitivity/specificity profiles) in detecting key immunotherapy-related biomarkers, such as PD-L1 expression, TMB, and microsatellite instability (MSI). Methods like Random Forest algorithms improve patient stratification for ICIs, thereby enhancing the personalization of treatments( 116 , 117 ). DL architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively process large-scale spatial and sequential datasets, enabling the identification of complex biomarkers, such as gene expression profiles predictive of ICI responsiveness. These AI approaches exhibit strong reliability, with AUC values ranging from 0.70 to 0.95, outperforming traditional, manual biomarker detection methods that tend to be slower, less precise, and less reproducible. Furthermore, AI’s capacity to integrate multi-omics data has revolutionized biomarker identification by uncovering intricate patterns that conventional techniques often miss( 118 , 119 ).

Accurately detecting the expression status of tumor molecular pathological markers is essential before initiating clinical immunotherapy, as these markers guide treatment decisions and predict patient responses( 120 ). For example, pembrolizumab (Keytruda) has gained FDA approval as a first-line treatment for patients with MSI-high (MSI-H) metastatic colorectal cancer, underscoring the clinical importance of such biomarkers. In addition to MSI, other genomic features, including TMB, homologous recombination deficiency (HRD), and whole-genome duplication carry significant predictive value( 120 – 122 ). Consequently, there is a growing demand for rapid and cost-effective biomarker detection methods, including approaches based on routine hematoxylin and eosin (H&E)-stained histopathology images, quantitative PCR (qPCR), immunohistochemistry (IHC), and next-generation sequencing (NGS), many of which do not require additional tissue samples( 38 ).

While MSI testing is clinically valuable, it is not always performed due to the need for supplementary genetic or immunohistochemical assays. Advances in DL have enabled the direct prediction of MSI status from widely available H&E-stained histology slides, with model performance benchmarked against clinically determined MSI reference standards—typically PCR-based MSI testing and/or IHC-defined mismatch repair deficiency (dMMR)—and with formalin-fixed, paraffin-embedded (FFPE) slides demonstrating higher predictive accuracy than snap-frozen specimens( 117 , 123 ). Additionally, circulating tumor DNA (ctDNA) analysis has been used to predict MSI status in endometrial cancer patients, thereby supporting informed decision-making for immunotherapy. Large multicenter studies using DL models trained on whole-slide images of colorectal tumors have achieved clinical-grade accuracy in detecting MSI and mismatch repair deficiency (dMMR), surpassing even the accuracy of expert pathologists. Nevertheless, challenges remain in generalizing these models across diverse datasets, partly due to variability in slide preparation and scanning protocols( 124 , 125 ). AI has also been effectively used to predict PD-L1 expression. ML algorithms, including random forest classifiers and generative adversarial networks, have been used to quantitatively assess PD-L1 expression in tumor cells with high agreement compared to pathologists’ manual scoring( 126 ). Fully automated CNNs have further enhanced tumor cell detection and PD-L1 tumor proportion scoring (TPS), improving diagnostic consistency and efficiency, particularly among less experienced pathologists. These AI-enabled methods hold promise for standardizing and accelerating PD-L1 evaluation in clinical practice( 127 , 128 ).

TMB is another pivotal biomarker associated with immunotherapy response, as elevated TMB correlates with a higher neoantigen load and stronger immune recognition. DL models such as Image2TMB have been developed to predict TMB status directly from H&E slides, potentially replacing costly and time-intensive whole-exome sequencing( 129 , 130 ). Moreover, multimodal DL approaches that integrate histopathological images with clinical data have improved the accuracy of TMB prediction in cancers such as colorectal carcinoma. Clinical variables, such as tumor stage and patient age, have also been shown to have significant associations with TMB status, emphasizing the benefit of combining molecular and clinical information to tailor personalized immunotherapy strategies( 130 , 131 ).

AI for antibody prediction

AI is accelerating antibody discovery in cancer immunotherapy by enabling faster and more cost-efficient design of therapeutic antibodies( 132 ). Advanced ML and DL models can predict antibody–antigen interactions, optimize binding affinity, and evaluate developability—key determinants of efficacy, safety, and manufacturability( 133 ). For example, AI-driven platforms such as ADCNet integrate protein and small-molecule information to predict antibody–drug conjugate activity. Generative approaches, including variational autoencoders and language models, are increasingly used to propose novel antibody sequences, often with an emphasis on complementarity-determining regions (CDRs) that govern binding specificity( 134 , 135 ). In this review, “ImmunoNet” (mentioned later) is used as a hypothetical placeholder for a multimodal AI framework unless explicitly linked to a specific published model. Sequence design and affinity maturation. Recent progress in AI and DL has substantially advanced antibody discovery, particularly in target binding screening and affinity maturation. While high-throughput sequencing(HTS) and display libraries can explore vast antibody sequence spaces, much of the potential diversity remains experimentally inaccessible( 136 , 137 ).

AI approaches, including neural networks and generative algorithms, have therefore been integrated with experimental workflows to predict and enhance antibody sequences, improving affinity and specificity. For instance, ensemble neural networks can outperform individual models in predicting sequence enrichment during phage display panning, and gradient-based frameworks such as Ens-Grad propose sequence modifications that yield superior binding compared with training-set antibodies. Deep mutational scanning paired with CNNs further supports rational design of combinatorial libraries, reducing the search space and improving discovery of high-affinity binders( 137 , 138 ). AI-driven affinity maturation can also streamline iterative optimization by forecasting productive mutations and prioritizing candidates using confidence estimates, thereby improving efficiency early discovery efficiency( 139 ).

Recent advances include LSTM-based deep generative models with downstream ranking (e.g., linear discriminant analysis) to generate and prioritize variants with progressively enhanced affinity and specificity, frequently surpassing frequency-only screening strategies( 140 ). Despite these gains, distribution shift and limited negative/failed examples remain practical constraints, underscoring the importance of prospective, experiment-in-the-loop validation when deploying AI-guided sequence optimization( 139 ). Structure prediction and antibody-antigen modeling. Recent advancements in DL have reduced reliance on labor-intensive and expensive experimental structure determination (e.g., crystallography, NMR, and cryo-electron microscopy).

However, antibody structure prediction remains challenging because antibodies exhibit high conformational variability, particularly in the CDR H3 loop, and general protein structure predictors can underperform in these regions( 66 , 141 ). Moreover, the limited availability and diversity of high-quality experimentally resolved antibody structures (especially antibody–antigen complexes) remains a persistent bottleneck for training, benchmarking, and generalizing antibody-specific structural models. To address these challenges, antibody-focused models can be grouped by function. For CDR-focused loop modeling, DeepH3 uses deep residual neural networks with geometric features to improve prediction of CDR H3 conformations, outperforming earlier approaches in benchmark evaluations( 142 , 143 ). For full variable-domain modeling (including side chains), DeepAb integrates unsupervised representation learning and attention mechanisms to enhance interpretability and accuracy, while DeepSCAb incorporates side-chain geometry prediction, which further refines structural forecasts( 144 , 145 ).

However, some approaches remain multi-step and lack equivariance, which can limit speed and flexibility. For fast, end-to-end CDR loop prediction with scalable throughput, ABlooper applies E(n)-equivariant graph neural networks and provides reliable quality assessment, making it suitable for large-scale repertoire analyses( 146 , 147 ). For nanobody-specific modeling, NanoNet directly predicts 3D backbone coordinates, enabling rapid, high-throughput structure prediction useful for antibody–nanobody docking and epitope mapping( 148 ). For integrated antibody–antigen pipelines, AbAdapt combines antibody and antigen modeling, epitope/paratope prediction, and rigid docking, leveraging state-of-the-art tools such as AlphaFold2 to accelerate antibody–antigen interaction studies(( 149 , 150 ). AI and ML are also transforming prediction and optimization of key pharmaceutical properties in therapeutic antibodies, addressing specificity, immunogenicity, aggregation, solubility, viscosity, and pharmacokinetics. Traditional experimental approaches for developability screening are limited by material requirements and throughput, complicating early-stage triage( 151 , 152 ).

AI-driven techniques, including DL and protein language models, now support rapid prediction of these properties directly from antibody sequences. Examples include bidirectional LSTM networks and transformer-based tools such as BioPhi for evaluating antibody “nativeness” and humanization needs, as well as solPredict and DeepSCM for quantitative prediction of solubility and viscosity using transfer learning and CNN-based models( 153 – 156 ). These approaches enable earlier elimination of candidates with unfavorable developability profiles, accelerating progression to downstream validation.

Beyond developability, AI models can support antibody optimization by simultaneously considering binding affinity and “naturalness”—which is associated with both key efficacy and safety. Deep contextual language models trained on affinity datasets can predict binding of novel variants and optimize functional and biophysical properties, outperforming purely random mutagenesis plus screening workflows( 157 , 158 ). AI can also integrate structural and in vitro assay data to predict pharmacokinetic behavior, identifying features such as isoelectric point and poly-specificity that influence antibody clearance. As more high-quality data becomes available, these ML methods are expected to continue improving, facilitating rational design of antibodies with superior therapeutic profiles and paving the way for safer, more effective biologic drugs ( 158 , 159 ). A summary of AI applications in the diagnosis, treatment planning, and management of autoimmune diseases is provided in Table 2 .

Table 2 AI Applications in Autoimmune Disease Management AI/ML Model or Algorithm Type Autoimmune Disease(s) Data Sources AI Application Purpose Performance Metrics Clinical or Translational Impact References CNNs RA, Psoriasis, SLE Radiographic (RA), Dermoscopic (Psoriasis), and Skin Images (SLE) Automated diagnosis, lesion classification, inflammation assessment AUC: 0.90–0.98; Accuracy: 91–96% Enables early detection and image-based disease grading; supports differential diagnosis ( 237 , 337 – 339 ) ANNs RA, SLE Clinical, lab, and serological data Predict treatment outcomes, disease classification, and remission likelihood AUC ~ 0.78–0.92; F1-score up to 0.91 Supports personalized therapy and disease severity stratification ( 340 , 341 ) SVMs MS Blood biomarkers (Vit D3, B12, Se) Diagnostic support Sensitivity ~ 98.98%, Accuracy ~ 98.89% Non-invasive, cost-effective diagnostic aid ( 342 ) XAI RA, SLE Multi-omics, HER, SNP Biomarker discovery, transparent decision support AUC > 90% Enhances interpretability; enables personalized clinical decisions ( 343 , 344 ) Random Forests & Gradient Boosting RA clinical/lab data Predict biologic therapy response AUC ~ 0.64–0.75 AUC ~ 0.64–0.75 ( 345 , 346 ) DL Models (DNNs, RNNs) RA, MS, T1D Psoriasis Imaging, genomics, clinical data Diagnosis, disease progression prediction, patient stratification AUC 0.80–0.92 Supports early detection and precision medicine; enables personalized interventions ( 249 , 347 – 352 ) NLP SLE, RA EHR, clinical notes Information extraction, risk assessment Boosted diagnosis accuracy Enhances structured data extraction from unstructured notes ( 353 – 355 ) Reinforcement Learning MS Force plate data (balance metrics) Disease severity assessment and modeling Qualitative improvement in balance modeling Aids in quantifying MS progression and motor control ( 356 ) Decision Trees & Ensemble Methods T1D, RA EHR, biometrics, CGM glucose logs, cytokine data Early diagnosis, complication prediction (e.g., hypoglycemia, RA activity) Accuracy 78–93% Supports timely diagnosis, predicts complications like hypoglycemia or disease flare ( 357 – 359 ) Transfer Learning RA Artificially augmented sparse datasets Synovitis classification for disease monitoring AUC 0.73–0.99 Enables AI application in data-scarce imaging scenarios ( 360 ) Digital Twin Models T1D, MS Multi-omics + clinical time series In silico therapy simulation and precision dosing Under validation; high simulation accuracy Enables pre-trial prediction of individualized treatment responses and disease progression ( 285 , 361 – 363 )

AI Applications in autoimmune disease diagnosis and therapy

The immune system is essential for protecting the body against foreign agents like bacteria, viruses and toxins. Upon detecting these invaders, the immune system mobilizes various defense cells to eliminate the threat. Normally, the immune system distinguishes between foreign entities and the body’s own cells. However, when this balance is disrupted and the immune system attacks healthy self-cells, autoimmune disorders occur( 18 ). These disorders can affect a wide range of organs and tissues, including blood vessels, connective tissue, endocrine glands (e.g., thyroid and pancreas), muscles, joints, skin, and red blood cells. To date, over 80 autoimmune diseases have been identified, with more likely undiscovered. Contributing factors include genetics, diet, infections, and chemical exposures. Diagnosing autoimmune diseases remains challenging due to the diversity of symptoms, which vary based on the affected organ or tissue and often include fatigue, redness, swelling, warmth, and pain( 160 ).

Given the complexity of their clinical manifestations and underlying pathophysiology, autoimmune diseases present significant diagnostic and therapeutic challenges. AI has shown transformative potential in this area, particularly in improving diagnostic accuracy, enabling personalized treatment strategies, and providing real-time disease monitoring( 161 ).

AI algorithms can process vast and diverse datasets, including EHRs, laboratory results, medical imaging, and genomic data( 21 ). By identifying subtle patterns within these datasets, AI supports earlier diagnosis, better disease prognosis and optimized therapeutic approaches( 162 ).

For example, ML-based EHR phenotyping has been used to identify SLE cohorts using structured codes and laboratory data, with NLP-derived narrative features evaluated as additional signals( 163 ). Similarly, AI analysis of OCT (including AS-OCT) can quantify intraocular inflammatory activity and assist uveitis grading, demonstrated correlation with Standardization of Uveitis Nomenclature (SUN) clinical grades and supporting earlier and more standardized assessment( 164 , 165 ). Despite its promise, challenges related to model development, data quality, and personalized treatment persist. Future progress is expected to rely on improved data integration and the refinement of AI algorithm, ultimately enhancing the diagnostic precision, treatment effectiveness, and disease monitoring. The application of AI in autoimmune disease research has grown considerably with advancements in AI technologies over the past two decades( 162 , 166 ). In the remainder of this section, we cover diagnostic applications, therapeutic applications (including drug discovery and treatment planning), and disease-specific examples in rheumatoid arthritis and type 1 diabetes.

Role of AI in diagnosing autoimmune disorders

AI, along with its associated models and algorithms, has become an invaluable asset in diagnosing autoimmune diseases, particularly in areas such as image analysis, laboratory data interpretation, and clinical decision support systems( 167 , 168 ). Furthermore, integrating AI with EHRs, wearable technologies, and patient-generated data has expanded the scope and precision of disease evaluation( 169 ). AI-based image analysis has shown significant promise in diagnosing autoimmune conditions, particularly RA and Immune-mediated dermatological diseases. In RA, ML and DL models can evaluate radiographs and ultrasound images to detect and quantify pathological features such as joint erosions and synovial hypertrophy, supporting earlier recognition and objective assessment of structural damage ( 170 , 171 ). Similarly, AI models can analyze images of skin lesions in autoimmune dermatological diseases, recognizing characteristic patterns and estimating the prevalence of these diseases. DL models trained on large dermoscopic or histopathological image repositories facilitate automatic detection of disease-specific features, assisting dermatologists in diagnosis and personalized treatment planning( 172 ). Representative performance has been reported for clinical image classification; for example, an EfficientNet-B4–based dermatology assistant achieved 91.4% sensitivity and 95.48% specificity for psoriasis when classifying clinical images among psoriasis, eczema/atopic dermatitis, and healthy skin in a labeled dataset( 173 ). AI also plays a pivotal role in analyzing laboratory data for the diagnosis of autoimmune diseases, particularly neurological disorders such as MS and neuromyelitis optica (NMO). AI-driven models analyze MRI scans to detect and quantify lesions in the brain and spinal cord, utilizing ML to discern patterns and structural abnormalities that differentiate among autoimmune neurological conditions( 174 – 177 ). Additionally, AI-based evaluation of serological markers, including autoantibody profiles, supports diagnosis and disease monitoring. For example, AI models that integrate patient-specific autoantibody data and clinical parameters can predict disease activity and risk of organ involvement in SLE, thereby assisting clinicians with personalized prognoses and decision-making( 178 , 179 ). AI-powered clinical decision support systems provide crucial assistance to healthcare providers in diagnosing autoimmune disorders. By synthesizing patient data, scientific literature, and AI-derived analyses, these systems generate evidence-based recommendations to improve diagnostic accuracy( 180 ).

These systems process EHR information, which includes patient history, symptoms, and laboratory results, to propose diagnostic hypotheses and suggest appropriate tests. They also assist in differential diagnosis. Additionally, these AI systems analyze longitudinal patient data, which encompasses disease progression, treatment responses, and adverse effects, to improve and refine diagnostic workflows( 181 ). Continuous learning from incoming data enables these platforms to dynamically update recommendations, contributing to the timely and individualized diagnosis of autoimmune diseases( 182 , 183 ).

The fusion of AI with EHRs, wearable devices, and patient-reported data enables a comprehensive assessment of autoimmune diseases. AI algorithms can extract hidden trends, correlations, and risk factors from EHR datasets( 184 , 185 ). Using natural language processing (NLP), AI can extract structured variables from unstructured clinical notes to support accurate diagnosis and prognosis. Wearable technologies, such as smartwatches and biosensors, continuously capture physiological data, including heart rate, temperature, and activity levels( 186 ). AI analyzes these data streams in context to detect disease flares, predict symptom exacerbations, and optimize therapeutic interventions. Patient-generated inputs—such as symptom diaries and self-reported outcomes—can also be processed by AI to enhance disease monitoring and diagnostic accuracy( 187 , 188 ). Applications of AI in autoimmune disease diagnosis span image interpretation, laboratory data analysis, and clinical decision support systems( 189 ). These applications demonstrate AI’s potential to enhance diagnostic precision, promote personalized medical care, and facilitate comprehensive disease evaluation ( 190 , 191 ). Algorithmic bias can arise when protected groups are underrepresented in training data (minority bias) or when deployment populations differ from training data (training–serving skew), reducing accuracy in those groups; therefore, subgroup performance reporting and equity-focused evaluation before and during deployment are essential( 192 ). As AI technologies advance, their integration into healthcare systems is expected to improve the efficiency and effectiveness of autoimmune disease diagnosis, ultimately leading to better patient outcomes( 191 ).

AI-driven drug discovery for autoimmune diseases

AI is reshaping drug development and precision medicine for autoimmune disorders, which are characterized by considerable heterogeneity in clinical symptoms and underlying biological mechanisms( 193 ). While contemporary treatment strategies are effective for some patients, they often fail to address individual variability, resulting in inconsistent therapeutic outcomes and unmet patient needs. AI-powered approaches, leveraging extensive multi-omics datasets and sophisticated computational techniques, are enabling deeper mechanistic insight into these complex diseases and facilitating the design of more personalized treatment strategies( 193 , 194 ). By integrating high-throughput omics technologies with AI and ML, detailed molecular profiles can be generated from large patient cohorts. Analysis of these profiles using ML algorithms facilitates patient stratification into subgroups that share common biological signatures( 195 ). Through clustering patients based on molecular and clinical characteristics, AI models can delineate distinct endotypes within autoimmune diseases, each characterized by specific dysregulated pathways (e.g., interferon signaling or acute-phase responses). This stratification is essential for customizing treatments according to the unique pathophysiological features of patient subgroups, moving beyond the traditional one-size-fits-all approach( 21 , 195 ).

AI-driven modeling has also advanced the discovery of therapeutic targets in autoimmune diseases by mapping genes and proteins that are differentially expressed in specific patient clusters and applying pathway enrichment and network analyses, researchers can identify master regulators and driver mutations instrumental in disease progression. These insights facilitate the selection of highly relevant and druggable targets, accelerating the development of more effective and safer therapies( 196 – 198 ). In the realm of drug development, AI is revolutionizing the design and optimization of novel drug candidates. Predictive models simulate interactions between millions of potential compounds and specific molecular targets, dramatically reducing the time and cost associated with conventional experimental methods( 199 , 200 ). A real-world illustration is AI-enabled drug repurposing, exemplified by BenevolentAI’s knowledge-graph approach that prioritized baricitinib for immune-mediated indications, highlighting how AI can accelerate hypothesis generation and candidate prioritization for downstream validation( 201 , 202 ). Moreover, AI-enabled “virtual patient” models enable in-silico assessment of drug efficacy and safety, allowing researchers to predict responses in individual patients or subgroups prior to clinical trials. However, these “virtual patient”/digital-twin approaches remain an emerging capability; their predictive performance and clinical utility require iterative experimental and clinical validation and are still being established across autoimmune indications( 203 ). This computational precision medicine approach holds great promise for delivering targeted therapies with improved benefit-risk profiles( 204 , 205 ). Importantly, the quality and interoperability of datasets used to train AI models are critical, alongside the need for standardized methodologies and well-characterized, sufficiently large patient cohorts. Ethical considerations—including data privacy, algorithmic bias, and transparency—must be rigorously addressed to ensure the equitable and trustworthy deployment of AI-based solutions( 188 , 206 ).

Looking forward, ongoing advancements in AI technologies, such as DL and digital twin modeling, are expected further to accelerate the transition to precision medicine in autoimmune disorders. These tools will enhance the monitoring of disease progression, prediction of treatment responses, and the real-time dynamic adjustment of therapeutic regimens( 207 , 208 ). The identification and validation of strong molecular and digital biomarkers will be crucial for enabling more precise patient stratification and disease monitoring, ultimately improving outcomes and quality of life for those affected by autoimmune diseases( 162 ).

AI in treatment planning and management

AI is rapidly reshaping treatment planning and management for autoimmune diseases, which are characterized by complex pathophysiology and substantial variability in clinical presentation and therapeutic response( 209 ). Conventional treatment approaches have primarily relied on empirical methods, often resulting in suboptimal outcomes, delayed interventions, and an increased risk of irreversible organ damage. AI-driven techniques, especially those employing ML and DL, can integrate and analyze extensive multidimensional datasets—spanning genetic, proteomic, imaging, and clinical data—to uncover novel biomarkers, stratify patients by risk, and predict disease progression with unprecedented precision( 161 , 193 ). For instance, ImmunoNet (a published DL framework) utilizes CNNs and multi-layer perceptrons (MLPs) to synthesize multi-omics and clinical information for disease classification, with up to 98% accuracy reported in the original study; however, this performance should be interpreted in the context of the study’s validation design and cohort characteristics, and independent external validation in diverse populations is needed to confirm generalizability ( 210 ).

One of AI’s most impactful contributions in autoimmune disorders lies in facilitating personalized treatment planning. By leveraging patient-specific data such as genetic profiles, molecular signatures, and longitudinal clinical records, AI algorithms like ImmunoNet can predict individual responses to therapies, including biologics, corticosteroids, and disease-modifying antirheumatic drugs (DMARDs), thereby reducing reliance on trial-and-error approaches( 211 , 212 ). In diseases such as RA and SLE, AI models can analyze immune cell phenotypes and genetic polymorphisms to stratify patients for targeted biologic therapies, enhancing efficacy while minimizing adverse effects. These systems also incorporate real-time data from wearable devices and EHRs to adjust treatment regimens, as exemplified in MS management dynamically. Such innovations are shifting clinical practice away from one-size-fits-all care toward truly individualized care( 213 – 215 ).

Computational intelligence methods have become indispensable tools for biomarker discovery, especially in complex autoimmune diseases. These techniques employ advanced algorithms, including fuzzy logic, genetic algorithms, artificial neural networks (ANNs), support vector machines (SVMs), and clustering methods, to analyze large, heterogeneous biological datasets derived from genomics, proteomics, metabolomics, and clinical records( 216 ). By integrating diverse data sources, computational intelligence can reveal hidden patterns and associations that may identify potential biomarkers linked to disease onset and progression. These approaches also support candidate biomarker validation through cross-validation and the development of predictive models to inform personalized medicine, treatment, and prevention strategies( 217 ).

In addition, modern deep-learning architectures—particularly transformer-based multimodal models that integrate medical imaging with routinely collected EHR variables (e.g., diagnostic codes, medications, and laboratory tests)—are increasingly used to capture complex temporal dependencies and improve representation learning in biomedical prediction tasks( 218 ).

AI is revolutionizing disease monitoring and clinical decision support through continuous analysis of clinical, laboratory, and imaging data. Platforms such as ImmunoNet utilize XAI techniques to provide transparent insights into disease activity, enabling early detection of flares and complications. Remote monitoring tools combined with AI algorithms analyze data streams from wearable devices to assess disease status outside clinical settings, thereby enhancing patient engagement and facilitating proactive interventions. This capability is particularly vital for relapsing-remitting diseases, such as SLE, where timely adjustments to immunomodulatory therapies or corticosteroids can prevent irreversible damage( 219 , 220 ).

Despite these advances, challenges remain, including data privacy concerns, algorithmic bias, and the opaque “black box” nature of some AI models. Federated learning approaches, implemented in frameworks such as ImmunoNet, help address privacy issues by decentralizing data processing, while XAI methods enhance transparency to support clinical adoption( 221 ). Interdisciplinary collaboration among clinicians, data scientists, and regulatory bodies is crucial for establishing ethical guidelines and robust validation frameworks. As these hurdles are overcome, AI is poised to transform autoimmune disease management, delivering precise, patient-centered care that enhances long-term outcomes and quality of life( 219 , 222 , 223 ).

AI in rheumatoid arthritis

RA is a chronic autoimmune condition primarily targeting synovial joints, resulting in inflammation and potential joint damage( 224 ). Affecting about 1% of the global population, RA is more common among women than men. Over the past five years, AI has made significant advancements in the field of rheumatology( 225 ). AI has rapidly become a transformative tool in managing RA, a complex autoimmune disease characterized by wide variability in clinical symptoms and treatment responses( 226 ). The inconsistent outcomes seen with advanced therapies, such as bDMARDs, highlight the urgent need for precision medicine approaches( 227 ). Leveraging large and diverse datasets, AI methodologies now provide the capability to move beyond empirical treatment decisions, offering personalized and more effective interventions that can enhance patient prognosis and quality of life. AI also facilitates the identification and modeling of factors influencing treatment response. By integrating extensive clinical datasets, electronic health records, and multi-omics data, AI algorithms uncover subtle patterns and associations often missed by conventional analyses( 193 , 226 , 227 ). For example, XAI methods, such as Shapley additive explanations (SHAP), have clarified the relative impact of clinical features on predicting remission among RA patients treated with biologics( 228 ). These insights support individualized treatment planning and facilitate the discovery of novel biomarkers and therapeutic targets, thereby advancing rheumatology research( 229 ). As illustrated in Fig. 4 , multimodal data—including clinical, imaging, histopathological, and molecular profiles—are integrated through AI methodologies such as ML, DL, and NLP. These models facilitate early diagnosis, optimize therapeutic strategies, and enable personalized RA care through accurate risk prediction and outcome modeling. The integration of AI in rheumatology is part of a broader digital transformation in healthcare, including electronic health records, telemedicine, wearable devices, and mobile health applications( 230 ). AI’s strength lies in processing and interpreting complex, large-scale data, enabling the identification of intricate patterns and relationships that exceed human capacity( 231 ). In RA, this capability is invaluable for modeling the complex interactions among genetic, clinical, and environmental factors influencing disease progression and treatment response ( 232 ). ML and DL algorithms have played a pivotal role in developing predictive models that inform risk stratification, prognosis, and therapeutic decisions( 233 ).

Fig. 4 Schematic representation of the AI-enabled pipeline for rheumatoid arthritis (RA) management. Multimodal data, including clinical records, multi-omics profiles, medical imaging, and histopathological features, are processed using ML, DL, and natural language processing (NLP) algorithms. These AI models support both diagnosis and personalized treatment planning, thereby contributing to the advancement of precision medicine in RA

One of AI’s key applications in RA involves enhancing diagnostic accuracy and disease classification. Traditional diagnosis relies on clinical assessment, laboratory tests, and imaging; however, AI models, such as ANNs and CNNs, have automated and improved these processes( 234 , 235 ). For instance, ANNs trained on demographic and serological data have achieved high accuracy in identifying RA patients. At the same time, CNNs applied to hand radiographs automate the detection of typical joint abnormalities, enabling earlier and more cost-effective diagnosis. These AI tools reduce human error and facilitate timely referral and intervention, which are critical to preventing disease progression( 236 – 238 ). AI also shows promise in predicting the response to methotrexate (MTX), a cornerstone of RA treatment( 239 ). One study used a penalized logistic regression model based on gene expression ratios before and after four weeks of MTX therapy, achieving an AUC of 0.78 in forecasting six-month treatment response( 240 ). Comparisons of ML methods—including Lasso regression, random forest, and XGBoost—indicate that although advanced algorithms may provide slight improvements, traditional logistic regression remains competitive, with AUCs ranging from 0.77 to 0.78( 241 , 242 ).

Beyond predicting pharmacologic response, AI helps identify important prognostic biomarkers and risk factors in RA( 209 ). For example, they are strong predictors of early response to tumor necrosis factor inhibitors (TNFi). These findings enhance patient stratification and facilitate clinicians in selecting the most optimal therapies, thereby reducing the reliance on trial-and-error approaches that have been historically common in RA treatment( 243 , 244 ).

AI is also poised to transform research in RA and drug development( 245 ). By mining clinical trial data, biomedical literature, and real-world evidence, AI models can identify new therapeutic targets, optimize trial designs, and predict treatment outcomes, thereby accelerating the development of novel therapies. The multi-omics integration of genetic, proteomic, and clinical data has shown promise in predicting responses to biologics and Janus kinase (JAK) inhibitors. However, challenges remain regarding data heterogeneity and integration( 246 – 248 ). Despite these advances, several challenges limit the full potential of AI in RA. The disease’s polygenic and multifactorial nature demands large and diverse datasets to develop robust, generalizable models. Variability in defining and measuring treatment responses, along with differing prediction timeframes across studies, complicates model comparison and clinical application. Moreover, issues of data quality, privacy, and ethics must be addressed to ensure the safe and effective deployment of AI in clinical practice( 193 , 249 , 250 ).

AI in type 1 diabetes

T1DM is a multifaceted autoimmune disease characterized by the infiltration of immune cells into pancreatic islets, resulting in the selective destruction of insulin-producing β-cells. This β-cell loss causes insulin deficiency and hyperglycemia, necessitating lifelong insulin replacement therapy( 251 ). AI has significantly advanced diabetes care by enabling earlier and more precise diagnoses, optimizing treatment approaches, and supporting real-time disease management( 252 ). AI and ML offer new avenues for early detection and prediction of diabetes, facilitating personalized interventions and improved outcomes. One critical application involves analyzing EHRs, which contain extensive patient data, including demographics, laboratory results, and medical history. AI/ML algorithms can detect patterns within this data that indicate diabetes or pre-diabetic conditions. Diabetes risk prediction models have been widely studied, with Schwarz et al. providing comprehensive analyses of their sensitivity and specificity( 253 , 254 ).

Accurate complication prediction is vital for targeted intervention to prevent or delay adverse outcomes( 255 ). AI-powered predictive analytics hold great promise for enhancing management of chronic conditions by supporting patients in maintaining healthy lifestyles, adhering to medications, and effectively monitoring glycemic control( 256 , 257 ). Various AI algorithms, such as support vector machines, logistic regression, neural networks, and decision trees, are utilized in diabetes prediction( 258 ).

These algorithms use diverse mathematical frameworks to recognize patterns and generate predictions. For example, Rajendra and Latifi applied logistic regression to the PIMA Indians Diabetes dataset (which primarily reflects T2DM risk rather than autoimmune T1DM) and a Vanderbilt dataset involving rural African Americans, achieving accuracies of 78% and 93%, respectively. Accordingly, predictive approaches based on PIMA (T2DM-oriented) should be distinguished from T1DM prediction models, which typically incorporate autoimmune/genetic markers (e.g., islet autoantibodies, HLA risk) and/or longitudinal glycemic trajectories. In addition, these accuracy values should be interpreted alongside sensitivity/specificity and AUC, and in the context of cohort characteristics (e.g., sample size, class balance, and population composition), which influence clinical utility and generalizability ( 259 ). Their findings highlight that factors beyond algorithm choice, such as thorough data preprocessing, removal of redundancies and missing values, normalization, cross-validation, feature selection, and ensemble methods, significantly impact model performance and efficiency( 259 , 260 ). Effective lifestyle management is a cornerstone of diabetes treatment, crucial for preventing complications and related health issues. Upon diagnosis, patients are encouraged to adopt healthier behaviors. Leveraging current technologies and extensive datasets, data-driven tools like Decision Support Systems (DSS) have been developed to empower patients and clinicians in diabetes management. These systems monitor diet, physical activity, medication adherence, glucose levels, and other relevant factors, thereby facilitating improved therapeutic outcomes( 261 ). As illustrated in Fig. 5 , AI technologies are revolutionizing T1DM care by enabling a comprehensive approach to disease prediction, real-time monitoring, and management. These systems support predictive modeling, guide individualized insulin therapy, inform lifestyle adjustments, and enhance clinical decision-making—ultimately driving more precise and responsive care tailored to each patient’s unique profile.

Fig. 5 Schematic overview of AI applications in T1D. AI models are utilized across various domains, including predictive modeling of disease progression, real-time health monitoring, clinical decision-making, and personalized patient management. Additional roles include optimizing treatment strategies, supporting public health interventions, and tailoring lifestyle and dietary recommendations to enhance patient outcomes and enable precision diabetes care

Drug discovery and development for T1DM increasingly rely on repurposing immunotherapies initially designed for other autoimmune diseases, transplantation, and cancer( 262 ). These therapies target immunological pathways, including the depletion of T- and B-cells, inhibition of cytokines, and enhancement of regulatory T-cells. While some drugs are guided by T1DM-specific genetic risk loci (e.g., CTLA4, IL2RA), others act on broader mechanisms, such as polyclonal anti-thymocyte globulin and JAK inhibitors( 263 – 265 ). Although these interventions have met primary clinical endpoints, they typically only transiently preserve endogenous C-peptide, underscoring the need for earlier, continuous, or combination therapies, as well as the development of agents with more durable effects. Advanced computational methods, particularly AI and ML, are being employed to identify promising drug candidates and predict off-target effects; however, few have progressed to clinical trials in T1DM to date( 266 , 267 ). Antigen-specific immunotherapies offer another promising strategy, with preliminary evidence indicating preservation of C-peptide levels in select T1DM subgroups( 268 , 269 ). The effectiveness of these therapies often depends on genetic factors such as HLA haplotypes, which influence immune responses. AI/ML tools are being integrated into quantitative trait locus analyses to model complex genetic effects on therapy response and predict peptides most likely presented by specific HLA molecules( 270 , 271 ). Unbiased immunopeptidome studies and advanced sequencing are identifying novel antigenic targets, while AI-driven databases and algorithms assist in discovering new immune receptor sequences and their antigen specificities. These advancements promise more precise immune tolerance induction strategies tailored to patient subpopulations( 272 – 274 ).

Combination therapies are gaining interest for prolonging therapeutic efficacy in T1D. Clinical trials combining agents with different mechanisms, such as anti-IL-21 with liraglutide, have shown potential by addressing both inflammation and β-cell function. In this specific setting, AI/ML can support anti-IL-21 + liraglutide combination trials by integrating immunologic and metabolic biomarkers (e.g., inflammatory signatures together with C-peptide and glycemic trajectories) to identify responder subgroups, enable biomarker-guided monitoring, and evaluate treatment interactions/synergy( 275 – 277 ). AI/ML-driven in silico models are increasingly used to predict drug interactions, toxicity, and synergistic effects, informing the design of safer and more effective combination treatments( 278 ). These computational approaches are also applied to optimize β-cell regeneration and immune modulation, leveraging insights from cancer and transplantation research. Future studies will likely integrate both in vitro and in silico methods to guide therapy selection and dosing, minimizing adverse effects while maximizing benefit( 279 , 280 ). Precision medicine in T1DM advances through identifying responder subgroups and customizing therapies based on patient-specific features. While conventional statistics have identified immune signatures of responders’ post-treatment, predictive markers guiding therapy selection before initiation are needed( 263 , 281 ). Demographic (age, ethnicity), genetic (HLA, pharmacogenetic markers), and environmental factors contribute to variable drug responses( 281 – 283 ). AI/ML models are being developed to integrate these factors, enabling the prediction of individual responses and the optimization of dosing regimens. The concept of “digital twins”—virtual patient models that incorporate genetic, immunological, and clinical data—holds promise for simulating therapeutic responses and refining precision medicine in T1D( 284 , 285 ).

Despite progress, challenges remain in clinically applying AI/ML to T1D therapy discovery. Ensuring model interpretability, generalizability, and actionability demands rigorous data management, interdisciplinary collaboration, and continuous expert engagement. The “black box” nature of many AI/ML approaches, DL, limits explainability, which is crucial for establishing clinical trust and adoption. Efforts to develop explainable AI (XAI) techniques aim to produce transparent outputs that clinicians and researchers can understand and validate. Bridging the gap between computational innovation and clinical relevance is essential to fully realize AI/ML’s potential in advancing T1D treatment( 286 – 288 ).

Algorithmic bias and equity in ai-driven immunotherapy

AI-enabled immunotherapy pipelines—spanning biomarker inference (e.g., PD-L1/TMB/MSI surrogates), response prediction, and immune-related adverse event (irAE) risk modeling—can amplify inequities when training and validation datasets do not reflect the populations intended for deployment( 289 ). A central driver is the uneven composition and documentation of health datasets (“health data poverty”), which may result from structural barriers to research participation and persistent underrepresentation of certain demographic groups; consequently, model performance can degrade in precisely those settings where access to specialist oncology/immunology care is already limited, widening disparities in treatment selection and outcomes ( 290 ).

Bias can be introduced across the full AI lifecycle. Sampling bias occurs when genomic resources, clinical cohorts, trial-linked biobanks underrepresent specific ancestries/ethnicities, geographic regions, socioeconomic strata, or comorbidity profiles, undermining generalizability. Measurement and label bias arise when reference standards (“ground truth”) are inconsistently defined or measured across institutions—for example, variability in PD-L1 staining/scoring practices or sequencing/variant-calling pipelines—leading models to learn site- or subgroup-specific artifacts rather than biology. In parallel, dataset (distribution) shift across scanners, staining protocols, and clinical workflows can produce subgroup-specific degradation even when overall headline performance appears acceptable( 290 – 292 ). In immuno-oncology, these concerns are particularly consequential because model outputs can influence high-stakes decisions such as ICI initiation, combination selection, and toxicity surveillance. When diversity in training/validation cohorts is limited or demographic reporting is incomplete, fairness and transportability become difficult to establish, increasing the risk of uneven benefit and harm across patient subgroups( 289 , 290 ). Equity risks also extend upstream: AI-enabled drug discovery and development workflows (including candidate prioritization and trial enrichment) can inherit biases from incomplete or unbalanced real-world and experimental datasets, which may distort discovery signals and limit downstream clinical applicability( 290 , 293 ). Mitigation therefore requires treating equity as a core performance dimension. Practical steps include documenting dataset composition and missingness, reporting stratified performance (e.g., calibration and AUC/sensitivity/specificity across relevant demographic and clinical subgroups), conducting external validation across institutions and workflows, and implementing post-deployment monitoring to detect drift and emergent subgroup harm. While fairness-aware learning and reweighting can help, they cannot substitute for representative data and consistent reference standards; governance and transparency are essential for equitable clinical translation( 290 , 292 ).

Future Perspective

The integration of AI into immunotherapy marks a transformative shift in precision medicine for both cancer and autoimmune diseases( 16 ). Looking ahead, several emerging directions are poised to reshape AI-driven Immunotherapeutics. In the near term, advancing explainable and interpretable AI will be essential for translating predictive models into clinically actionable tools. By making algorithmic outputs transparent and interpretable, AI will foster greater trust among clinicians, accelerate regulatory approval, and facilitate real-time decision-making in diverse clinical settings( 39 , 294 ).

However, routine clinical adoption remains limited not by model capability alone, but by translation barriers, most notably insufficient external validation across institutions and scanners/platforms, vulnerability to dataset shift, limited prospective and impact-focused clinical trials, and unresolved regulatory and clinical-evaluation requirements for AI deployed as software-as-a-medical-device (SaMD). These include a clear definition of intended use, clinically appropriate reference standards (“ground truth”), robust strategies for model updating and post-deployment performance monitoring, and ongoing surveillance for drift and safety( 295 , 296 ).

In parallel, immunotherapy relies on diverse datasets, including multi-omics, high-resolution imaging, and longitudinal clinical records, which must be integrated and harmonized across platforms and institutions( 52 ). The rise of federated learning and privacy-preserving computation offers promising solutions, enabling collaborative model training without compromising patient privacy( 297 ).

Yet, even with federated approaches, clinical-grade deployment requires standardized pre-analytic and analytic pipelines (e.g., acquisition/scanning protocols, sequencing batch correction, and complete metadata capture), rigorous quality control, and interoperable data governance to ensure that “real-world” inputs remain reliable enough for decision support ( 298 , 299 ).

Over the longer term, digital twin technologies and in silico clinical trials are emerging as next-generation tools for therapy simulation( 300 ). These patient-specific virtual models can dynamically predict disease trajectories and treatment responses, enabling preemptive optimization of dosing, safety, and efficacy before real-world administration( 301 ). For example, in autoimmune disease, digital-twin (‘virtual patient’) models have been proposed for type 1 diabetes to simulate patient-specific glucose–insulin dynamics and immune–metabolic trajectories, supporting individualized insulin optimization and the in silico evaluation of immunomodulatory strategies; however, prospective clinical validation remains necessary before routine use( 285 ).

This paradigm holds particular promise in reducing clinical trial costs and timelines( 302 ), but its clinical translation will depend on demonstrating reproducible benefit against clinically meaningful endpoints in prospective evaluations, and meeting expectations for transparent verification/validation and safety monitoring (e.g., clearly defined intended use and reference standards, reporting algorithm versioning and input-data handling, systematic error analysis, and post-deployment surveillance for performance drift)( 303 ). Additionally, the convergence of AI with next-generation biomedical technologies, such as single-cell transcriptomics, spatial omics, and 3D imaging, will uncover new layers of immune complexity. These integrative approaches are expected to reveal novel biomarkers, resistance mechanisms, and combinatorial therapeutic strategies, thereby expanding the therapeutic arsenal available to clinicians( 304 ).

Ethical, legal, and societal considerations must evolve in harmony with technical advancements. Addressing algorithmic bias, ensuring data representativeness across populations, and developing transparent regulatory pathways will be essential for equitable AI deployment. Multidisciplinary collaboration among clinicians, data scientists, policymakers, and patient communities will be critical to ensuring responsible and inclusive innovation. In summary, the future of AI in immunotherapy will be defined not only by methodological advances, but also by robust validation, regulatory readiness, and implementation science that enable safe, effective, and scalable deployment across real-world clinical systems.

Conclusion

AI is catalyzing a paradigm shift in immunotherapy, transforming the diagnosis, treatment, and monitoring of cancer and autoimmune diseases( 16 ). By leveraging advanced ML and DL algorithms, AI enables the integration of complex biomedical data, including multi-omics, imaging, and real-world clinical records, into cohesive, clinically actionable insights( 305 ). This data-driven transformation is propelling immunotherapy toward unprecedented levels of personalized treatment and efficacy( 119 ).

AI has accelerated the identification of predictive biomarkers, including PD-L1, TMB, MSI, and neoantigen profiles in oncology( 16 , 33 ). These advances have improved patient stratification and enabled more precise deployment of ICIs( 306 ). AI has also enhanced the design and monitoring of CAR-T cell therapies and antibody-based biopharmaceuticals by simulating molecular interactions, predicting toxicity, and optimizing dosing strategies, thereby shortening development timelines and improving safety profiles( 65 ). In the realm of autoimmune disease, AI has deepened our understanding of disease heterogeneity, facilitated early diagnosis, and enabled personalized treatment( 166 ). Through multi-omic analysis, wearable data integration, and EHR mining, AI supports longitudinal disease monitoring and drug repurposing in conditions such as RA, SLE, MS, and T1DM( 21 ). These capabilities not only improve treatment outcomes but also open avenues for preventive and predictive care.

Despite these advancements, critical barriers remain. Data quality limitations, such as noise in single-cell RNA sequencing, batch effects in imaging, and inconsistencies in digital pathology staining, can compromise model performance. Inadequate quality control and dataset shifts across platforms exacerbate these issues, making model validation across institutions challenging. Equally important are interoperability challenges, insufficient standardization, and lack of regulatory frameworks for AI models, which remain significant hurdles to clinical adoption. Additionally, ethical considerations—including patient privacy, informed consent, accountability, and equitable access—must be embedded in the development and deployment of AI systems to ensure responsible translation into practice.

In conclusion, AI is a transformative force in immunotherapy, enhancing diagnostic accuracy, optimizing therapeutic efficacy, and advancing precision medicine. However, to fully realize its potential in clinical practice, continued investment in data infrastructure, model validation, and interdisciplinary collaboration will be essential. Ultimately, the goal is not just smarter algorithms—but more equitable, accessible, and human-centered care for all patients, everywhere.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jamal Alshorman and Mohammad Javad Mehran contributed equally to this work.

Author contributions

Alshorman J.: Conceptualization, Methodology, Writing – Original Draft, Visualization. Mehran M.J.: Methodology, Formal analysis, Writing – Original Draft. Bahrami Y.: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – Review & Editing. Mohammadzadeh S.: Literature search, Writing – Review & Editing, Visualization. Barzigar R.: Formal analysis, Writing – Review & Editing. Morshedi M.: Literature search, Visualization. Haider K.H.: Literature search, Writing – Review & Editing. Trmbo K.M.: Visualization, Writing – Review & Editing. Rong S.-J.: Supervision, Validation. Jadgal N.: Literature search, Writing – Review & Editing. Altahla R.: Formal analysis, Writing – Review & Editing. Bolideei M.: Supervision, Project administration, Funding acquisition, Writing – Review & Editing. Wang Y.: Supervision, Funding acquisition, Writing – Review & Editing.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare no conflicts of interest.

Clinical trial number

Not applicable.

References

1. Varadé J Magadán S González-Fernández Á Human immunology and immunotherapy: main achievements and challenges Cell Mol Immunol 2021 18 4 805 28 10.1038/s41423-020-00530-6 32879472 PMC7463107 Varadé J, Magadán S, González-Fernández Á. Human immunology and immunotherapy: main achievements and challenges. Cell Mol Immunol. 2021;18(4):805–28. 32879472 10.1038/s41423-020-00530-6 PMC7463107

2. Han S Shuen WH Wang W-W Nazim E Toh HC Tailoring precision immunotherapy: coming to a clinic soon? ESMO Open 2020 5 e000631 10.1136/esmoopen-2019-000631 33558033 PMC7046383 Han S, Shuen WH, Wang W-W, Nazim E, Toh HC. Tailoring precision immunotherapy: coming to a clinic soon? ESMO Open. 2020;5:e000631. 33558033 10.1136/esmoopen-2019-000631 PMC7046383

3. Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre MF et al. The global burden of cancer 2013. JAMA Oncol.1(4):505–27. 10.1001/jamaoncol.2015.0735 PMC4500822 26181261

4. Balakrishna A, Nahrwold D, Hughes C. Intensive Care of Cancer Patients. Anesthesia for Oncological Surgery. Cham: Springer International Publishing. pp. 457–70.

5. Boussios S, Pentheroudakis G, Katsanos K, Pavlidis N. Systemic treatment-induced gastrointestinal toxicity: incidence, clinical presentation, and management. Annals Gastroenterol.25(2):106&#8211. PMC3959393 24713845

6. Xie J Zheng Z Tuo L Deng X Tang H Peng C Recent advances in exosome-based immunotherapy applied to cancer Front Immunol 2023 14 1296857 10.3389/fimmu.2023.1296857 38022585 PMC10662326 Xie J, Zheng Z, Tuo L, Deng X, Tang H, Peng C, et al. Recent advances in exosome-based immunotherapy applied to cancer. Front Immunol. 2023;14:1296857. 38022585 10.3389/fimmu.2023.1296857 PMC10662326

7. Xu Z, Zeng S, Gong Z, Yan Y. Exosome-based immunotherapy: a promising approach for cancer treatment. Mol Cancer.19:1–16. 10.1186/s12943-020-01278-3 PMC7661275 33183286

8. Ye Y Zhang Y Yang N Gao Q Ding X Kuang X Profiling of immune features to predict immunotherapy efficacy The Innovation 2022 10.1016/j.xinn.2021.100194 34977836 PMC8688727 Ye Y, Zhang Y, Yang N, Gao Q, Ding X, Kuang X, et al. Profiling of immune features to predict immunotherapy efficacy. The Innovation. 2022. 10.1016/j.xinn.2021.100194. 34977836 10.1016/j.xinn.2021.100194 PMC8688727

9. Nishino M Ramaiya NH Hatabu H Hodi FS Monitoring immune-checkpoint blockade: response evaluation and biomarker development Nat Rev Clin Oncol 2017 14 11 655 68 10.1038/nrclinonc.2017.88 28653677 PMC5650537 Nishino M, Ramaiya NH, Hatabu H, Hodi FS. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol. 2017;14(11):655–68. 28653677 10.1038/nrclinonc.2017.88 PMC5650537

10. Li T Li Y Zhu X He Y Wu Y Ying T Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction Semin Cancer Biol 2023 91 50 69 10.1016/j.semcancer.2023.02.007 36870459 Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, et al. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol. 2023;91:50–69. 36870459 10.1016/j.semcancer.2023.02.007

11. Xie J Luo X Deng X Tang Y Tian W Cheng H Advances in Artificial Intelligence to predict cancer immunotherapy efficacy Front Immunol 2023 13 1076883 10.3389/fimmu.2022.1076883 36685496 PMC9845588 Xie J, Luo X, Deng X, Tang Y, Tian W, Cheng H, et al. Advances in Artificial Intelligence to predict cancer immunotherapy efficacy. Front Immunol. 2023;13:1076883. 36685496 10.3389/fimmu.2022.1076883 PMC9845588

12. Weber JK Morrone JA Kang SG Zhang L Lang L Chowell D Unsupervised and supervised AI on molecular dynamics simulations reveal complex characteristics of HLA-A2-peptide immunogenicity Brief Bioinform 2024 25 1 504 10.1093/bib/bbad504 PMC10793977 38233090 Weber JK, Morrone JA, Kang SG, Zhang L, Lang L, Chowell D, et al. Unsupervised and supervised AI on molecular dynamics simulations reveal complex characteristics of HLA-A2-peptide immunogenicity. Brief Bioinform. 2024;25(1):504. 10.1093/bib/bbad504 PMC10793977 38233090

13. Yang Y, Zhao Y, Liu X, Huang J. Artificial intelligence for the prediction of response to cancer immunotherapy. Seminars in Cancer Biology. 87: Academic Press. pp. 137–47. 10.1016/j.semcancer.2022.11.008 36372326

14. D’Orsi L Capasso B Lamacchia G Pizzichini P Ferranti S Liverani A Recent advances in artificial intelligence to improve immunotherapy and the use of digital twins to identify prognosis of patients with solid tumors Int J Mol Sci 2024 25 21 11588 10.3390/ijms252111588 39519142 PMC11546512 D’Orsi L, Capasso B, Lamacchia G, Pizzichini P, Ferranti S, Liverani A, et al. Recent advances in artificial intelligence to improve immunotherapy and the use of digital twins to identify prognosis of patients with solid tumors. Int J Mol Sci. 2024;25(21):11588. 39519142 10.3390/ijms252111588 PMC11546512

15. Costa Simões JM. A Machine Learning Approach for Predicting Microsatellite Instability using RNAseq.

16. Olawade DB, Clement David-Olawade A, Adereni T, Egbon E, Teke J, Boussios S. Integrating AI into Cancer Immunotherapy—A. Narrative Rev Curr Appl Future Dir Dis.13(1):24&#8211. 10.3390/diseases13010024 PMC11764268 39851488

17. Afzal M Sah AK Agarwal S Tanzeel A Elshaikh RH Alobeidli FA Advancements in the treatment of autoimmune diseases: integrating artificial intelligence for personalized medicine Trends in Immunotherapy 2024 10.24294/ti8970 Afzal M, Sah AK, Agarwal S, Tanzeel A, Elshaikh RH, Alobeidli FA, et al. Advancements in the treatment of autoimmune diseases: integrating artificial intelligence for personalized medicine. Trends in Immunotherapy. 2024. 10.24294/ti8970.

18. Davidson A Diamond B Autoimmune diseases N Engl J Med 2001 345 5 340 50 10.1056/NEJM200108023450506 11484692 Davidson A, Diamond B. Autoimmune diseases. N Engl J Med. 2001;345(5):340–50. 11484692 10.1056/NEJM200108023450506

19. Ngo ST Steyn FJ McCombe PA Gender differences in autoimmune disease Front Neuroendocrinol 2014 35 3 347 69 10.1016/j.yfrne.2014.04.004 24793874 Ngo ST, Steyn FJ, McCombe PA. Gender differences in autoimmune disease. Front Neuroendocrinol. 2014;35(3):347–69. 24793874 10.1016/j.yfrne.2014.04.004

20. Wang L Khunsriraksakul C Markus H Chen D Zhang F Chen F Integrating single-cell expression quantitative trait loci summary statistics to understand complex trait risk genes Nature communications 2024 15 1 4260 10.1038/s41467-024-48143-1 38769300 PMC11519974 Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, et al. Integrating single-cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nature communications. 2024;15(1):4260. 38769300 10.1038/s41467-024-48143-1 PMC11519974

21. Stafford IS Kellermann M Mossotto E Beattie RM MacArthur BD Ennis S A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases NPJ Digit Med 2020 3 1 30 10.1038/s41746-020-0229-3 32195365 PMC7062883 Stafford IS, Kellermann M, Mossotto E, Beattie RM, MacArthur BD, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digit Med. 2020;3(1):30. 32195365 10.1038/s41746-020-0229-3 PMC7062883

22. Nwosu BU Maranda L Cullen K Greenman L Fleshman J McShea N A randomized, double-blind, placebo-controlled trial of adjunctive metformin therapy in overweight/obese youth with type 1 diabetes PLoS One 2015 10 9 e0137525 10.1371/journal.pone.0137525 26367281 PMC4569440 Nwosu BU, Maranda L, Cullen K, Greenman L, Fleshman J, McShea N, et al. A randomized, double-blind, placebo-controlled trial of adjunctive metformin therapy in overweight/obese youth with type 1 diabetes. PLoS One. 2015;10(9):e0137525. 26367281 10.1371/journal.pone.0137525 PMC4569440

23. Lund SS Tarnow L Astrup AS Hovind P Jacobsen PK Alibegovic AC Effect of adjunct metformin treatment in patients with type-1 diabetes and persistent inadequate glycaemic control. A randomized study PLoS One 2008 3 10 e3363 10.1371/journal.pone.0003363 18852875 PMC2566605 Lund SS, Tarnow L, Astrup AS, Hovind P, Jacobsen PK, Alibegovic AC, et al. Effect of adjunct metformin treatment in patients with type-1 diabetes and persistent inadequate glycaemic control. A randomized study. PLoS One. 2008;3(10):e3363. 18852875 10.1371/journal.pone.0003363 PMC2566605

24. Rider NL Srinivasan R Khoury P Artificial intelligence and the hunt for immunological disorders Curr Opin Allergy Clin Immunol 2020 20 6 565 73 10.1097/ACI.0000000000000691 33002894 PMC7908683 Rider NL, Srinivasan R, Khoury P. Artificial intelligence and the hunt for immunological disorders. Curr Opin Allergy Clin Immunol. 2020;20(6):565–73. 33002894 10.1097/ACI.0000000000000691 PMC7908683

25. Saxena S. OnionMHC: a deep learning model for peptide-HLA-A* 02: 01 binding predictions.

26. Peng X, Yang D, Zhou Y, Peng S. TlcMHCpan: A Novel Deep Learning Model for Enhanced Pan-Specific Prediction of Peptide-HLA Binding: IEEE Access.

27. Liu Z Cui Y Xiong Z Nasiri A Zhang A Hu J DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction Sci Rep 2019 9 1 794 10.1038/s41598-018-37214-1 30692623 PMC6349913 Liu Z, Cui Y, Xiong Z, Nasiri A, Zhang A, Hu J. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction. Sci Rep. 2019;9(1):794. 30692623 10.1038/s41598-018-37214-1 PMC6349913

28. Su Z Wu Y Cao K Du J Cao L Wu Z Apex-phla: a novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules Methods 2024 228 38 47 10.1016/j.ymeth.2024.05.013 38772499 Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, et al. Apex-phla: a novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods. 2024;228:38–47. 38772499 10.1016/j.ymeth.2024.05.013

29. Bottomly D McWeeney S Just how transformative will AI/ML be for immuno-oncology?> J Immunother Cancer 2024 12 3 7841 10.1136/jitc-2023-007841 PMC10966790 38531545 Bottomly D, McWeeney S. Just how transformative will AI/ML be for immuno-oncology? J Immunother Cancer. 2024;12(3):7841. 10.1136/jitc-2023-007841 PMC10966790 38531545

30. Afkham SA Mirdehghan S Utopia Can a New Era of AI Predict Immunotherapy Efficacy? Handbook of Cancer and Immunology 2070 Cham Springer Nature Switzerland 1 17 Afkham SA, Mirdehghan S, Utopia. Can a New Era of AI Predict Immunotherapy Efficacy? Handbook of Cancer and Immunology. Cham: Springer Nature Switzerland; 2070. pp. 1–17.

31. Dercle L McGale J Sun S Marabelle A Yeh R Deutsch E Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy J Immunother Cancer 2022 10 9 5292 10.1136/jitc-2022-005292 PMC9528623 36180071 Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer. 2022;10(9):5292. 10.1136/jitc-2022-005292 PMC9528623 36180071

32. Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD et al. Artificial Intelligence-based Radiomics in the Era of Immuno-oncology. Oncologist.27(6):471–83. 10.1093/oncolo/oyac036 PMC9177100 35348765

33. Prelaj A Miskovic V Zanitti M Trovo F Genova C Viscardi G Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review Ann Oncol 2024 35 1 29 65 10.1016/j.annonc.2023.10.125 37879443 Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol. 2024;35(1):29–65. 37879443 10.1016/j.annonc.2023.10.125

34. Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer.10(7):4848&#8211. 10.1136/jitc-2022-004848 PMC9260846 35793875

35. Sako C Duan C Maresca K Kent S Schmidt TG Aerts H Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer JCO Clin Cancer Inf 2024 8 e2400133 10.1200/CCI.24.00133 PMC11658027 39671539 Sako C, Duan C, Maresca K, Kent S, Schmidt TG, Aerts H, et al. Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer. JCO Clin Cancer Inf. 2024;8:e2400133. 10.1200/CCI.24.00133 PMC11658027 39671539

36. Hill DLG AI in imaging: the regulatory landscape Br J Radiol 2024 97 1155 483 91 10.1093/bjr/tqae002 38366148 PMC11027239 Hill DLG. AI in imaging: the regulatory landscape. Br J Radiol. 2024;97(1155):483–91. 38366148 10.1093/bjr/tqae002 PMC11027239

37. El Naqa I Karolak A Luo Y Folio L Tarhini AA Rollison D Translation of AI into oncology clinical practice Oncogene 2023 42 42 3089 97 10.1038/s41388-023-02826-z 37684407 PMC12516697 El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, et al. Translation of AI into oncology clinical practice. Oncogene. 2023;42(42):3089–97. 37684407 10.1038/s41388-023-02826-z PMC12516697

38. Li T, Li Y, Zhu X, He Y, Wu Y, Ying T et al. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design, and immunotherapy response prediction. Seminars in Cancer Biology. 91: Academic Press. pp. 50–69. 10.1016/j.semcancer.2023.02.007 36870459

39. Yoo SK, Fitzgerald CW, Cho BA, Fitzgerald BG, Han C, Koh ES et al. Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data. Nature Medicine.1–12. 10.1038/s41591-024-03398-5 PMC11922749 39762425

40. Rehmani MU Aggarwal S Prasad CP Singh M Dissecting the tumor microenvironment (TME) to decipher new immunotherapy targets by using artificial intelligence Asian Pac J Cancer Care 2024 9 4 793 9 Rehmani MU, Aggarwal S, Prasad CP, Singh M. Dissecting the tumor microenvironment (TME) to decipher new immunotherapy targets by using artificial intelligence. Asian Pac J Cancer Care. 2024;9(4):793–9.

41. Xu Z Wang X Zeng S Ren X Yan Y Gong Z Applying artificial intelligence for cancer immunotherapy Acta Pharm Sin B 2021 11 11 3393 405 10.1016/j.apsb.2021.02.007 34900525 PMC8642413 Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B. 2021;11(11):3393–405. 34900525 10.1016/j.apsb.2021.02.007 PMC8642413

42. Zhang ZHE, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Seminars in Cancer Biology. 90: Academic Press. pp. 57–72. 10.1016/j.semcancer.2023.02.005 36796530

43. Weerarathna IN, Kamble AR, Luharia A. Artificial intelligence applications for biomedical cancer research: a review. Cureus.15(11). 10.7759/cureus.48307 PMC10697339 38058345

44. Elkhader J, Elemento O. Artificial intelligence in oncology: From bench to clinic. Seminars in Cancer Biology. 84: Academic Press. pp. 113–28. 10.1016/j.semcancer.2021.04.013 33915289

45. Pardoll DM The blockade of immune checkpoints in cancer immunotherapy Nat Rev Cancer 2012 12 4 252 64 10.1038/nrc3239 22437870 PMC4856023 Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252–64. 22437870 10.1038/nrc3239 PMC4856023

46. Zou W Chen L Inhibitory B7-family molecules in the tumour microenvironment Nat Rev Immunol 2008 8 6 467 77 10.1038/nri2326 18500231 Zou W, Chen L. Inhibitory B7-family molecules in the tumour microenvironment. Nat Rev Immunol. 2008;8(6):467–77. 18500231 10.1038/nri2326

47. Ribas A Wolchok JD Cancer immunotherapy using checkpoint blockade Science 2018 359 6382 1350 5 10.1126/science.aar4060 29567705 PMC7391259 Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science. 2018;359(6382):1350–5. 29567705 10.1126/science.aar4060 PMC7391259

48. Addala V Newell F Pearson JV Redwood A Robinson BW Creaney J Computational immunogenomic approaches to predict response to cancer immunotherapies Nat Rev Clin Oncol 2024 21 1 28 46 10.1038/s41571-023-00830-6 37907723 Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, et al. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol. 2024;21(1):28–46. 37907723 10.1038/s41571-023-00830-6

49. Alsaab HO Sau S Alzhrani R Tatiparti K Bhise K Kashaw SK PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome Front Pharmacol 2017 8 561 10.3389/fphar.2017.00561 28878676 PMC5572324 Alsaab HO, Sau S, Alzhrani R, Tatiparti K, Bhise K, Kashaw SK, et al. PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome. Front Pharmacol. 2017;8:561. 28878676 10.3389/fphar.2017.00561 PMC5572324

50. Shamai G Livne A Polónia A Sabo E Cretu A Bar-Sela G Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer Nat Commun 2022 13 1 6753 10.1038/s41467-022-34275-9 36347854 PMC9643479 Shamai G, Livne A, Polónia A, Sabo E, Cretu A, Bar-Sela G, et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat Commun. 2022;13(1):6753. 36347854 10.1038/s41467-022-34275-9 PMC9643479

51. Choi S, Cho SI, Ma M, Park S, Pereira S, Aum BJ et al. Artificial intelligence–powered programmed death ligand one analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response. Eur J Cancer.170:17–26. 10.1016/j.ejca.2022.04.011 35576849

52. Vanguri RS Luo J Aukerman AT Egger JV Fong CJ Horvat N Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer Nat Cancer 2022 3 10 1151 64 10.1038/s43018-022-00416-8 36038778 PMC9586871 Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022;3(10):1151–64. 36038778 10.1038/s43018-022-00416-8 PMC9586871

53. Cheong JH Wang SC Park S Porembka MR Christie AL Kim H Development and validation of a prognostic and predictive 32-gene signature for gastric cancer Nat Commun 2022 13 1 774 10.1038/s41467-022-28437-y 35140202 PMC8828873 Cheong JH, Wang SC, Park S, Porembka MR, Christie AL, Kim H, et al. Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nat Commun. 2022;13(1):774. 35140202 10.1038/s41467-022-28437-y PMC8828873

54. Gong X Karchin R Pan-cancer HLA gene-mediated tumor immunogenicity and immune evasion Mol Cancer Res 2022 20 8 1272 83 10.1158/1541-7786.MCR-21-0886 35533264 PMC9357147 Gong X, Karchin R. Pan-cancer HLA gene-mediated tumor immunogenicity and immune evasion. Mol Cancer Res. 2022;20(8):1272–83. 35533264 10.1158/1541-7786.MCR-21-0886 PMC9357147

55. Wang L Zhang H Pan C Yi J Cui X Li N Predicting durable responses to immune checkpoint inhibitors in non-small-cell lung cancer using a multi-feature model Front Immunol 2022 13 829634 10.3389/fimmu.2022.829634 35529874 PMC9072668 Wang L, Zhang H, Pan C, Yi J, Cui X, Li N, et al. Predicting durable responses to immune checkpoint inhibitors in non-small-cell lung cancer using a multi-feature model. Front Immunol. 2022;13:829634. 35529874 10.3389/fimmu.2022.829634 PMC9072668

56. Pan X Zhang C Wang J Wang P Gao Y Shang S Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma Brief Bioinform 2022 23 1 481 10.1093/bib/bbab481 34864866 Pan X, Zhang C, Wang J, Wang P, Gao Y, Shang S, et al. Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma. Brief Bioinform. 2022;23(1):481. 10.1093/bib/bbab481 34864866

57. Dercle L Zhao B Gönen M Moskowitz CS Firas A Beylergil V Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis JAMA Oncol 2022 8 3 385 92 10.1001/jamaoncol.2021.6818 35050320 PMC8778619 Dercle L, Zhao B, Gönen M, Moskowitz CS, Firas A, Beylergil V, et al. Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis. JAMA Oncol. 2022;8(3):385–92. 35050320 10.1001/jamaoncol.2021.6818 PMC8778619

58. Paidi SK Rodriguez Troncoso J Raj P Monterroso Diaz P Ivers JD Lee DE Raman spectroscopy and machine learning reveal early tumor microenvironmental changes induced by immunotherapy Cancer Res 2021 81 22 5745 55 10.1158/0008-5472.CAN-21-1438 34645610 PMC8841097 Paidi SK, Rodriguez Troncoso J, Raj P, Monterroso Diaz P, Ivers JD, Lee DE, et al. Raman spectroscopy and machine learning reveal early tumor microenvironmental changes induced by immunotherapy. Cancer Res. 2021;81(22):5745–55. 34645610 10.1158/0008-5472.CAN-21-1438 PMC8841097

59. Tang T Huang X Zhang G Hong Z Bai X Liang T Advantages of targeting the tumor immune microenvironment over blocking immune checkpoints in cancer immunotherapy Signal Transduct Target Ther 2021 6 1 72 10.1038/s41392-020-00449-4 33608497 PMC7896069 Tang T, Huang X, Zhang G, Hong Z, Bai X, Liang T. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoints in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72. 33608497 10.1038/s41392-020-00449-4 PMC7896069

60. Park S Ock CY Kim H Pereira S Park S Ma M Artificial intelligence–powered spatial analysis of tumor-infiltrating lymphocytes as a complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer J Clin Oncol 2022 40 17 1916 28 10.1200/JCO.21.02010 35271299 PMC9177249 Park S, Ock CY, Kim H, Pereira S, Park S, Ma M, et al. Artificial intelligence–powered spatial analysis of tumor-infiltrating lymphocytes as a complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer. J Clin Oncol. 2022;40(17):1916–28. 35271299 10.1200/JCO.21.02010 PMC9177249

61. Zhang Z Chen L Chen H Zhao J Li K Sun J Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy EBioMedicine 2022 10.1016/j.ebiom.2022.104207 35961204 PMC9382263 Zhang Z, Chen L, Chen H, Zhao J, Li K, Sun J, et al. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy. EBioMedicine. 2022. 10.1016/j.ebiom.2022.104207. 35961204 10.1016/j.ebiom.2022.104207 PMC9382263

62. Mo CK, Liu J, Chen S, Storrs E, Costa ALN, Houston A et al. Tumour evolution and microenvironment interactions in 2D and 3D space. Nature.634(8036):1178–86. 10.1038/s41586-024-08087-4 PMC11525187 39478210

63. Özen M, Gündüz M, Haider H. Chimeric Antigen Receptor (CAR) T-Cells as a Therapeutic Modality. In: Haider H, editor Stem Cells: From Hype to. 8. Hope, WSP Company, Singapore, Chapter. p. 211–36.

64. Luciani F, Safavi A, Guruprasad P, Chen L, Ruella M. Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges. 10.1158/2643-3230.BCD-23-0240 PMC12050963 40152695

65. Shahzadi M Rafique H Waheed A Naz H Waheed A Zokirova FR Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives Ther Adv Vaccines Immunother 2024 12 25151355241305856 10.1177/25151355241305856 39691280 PMC11650588 Shahzadi M, Rafique H, Waheed A, Naz H, Waheed A, Zokirova FR, et al. Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives. Ther Adv Vaccines Immunother. 2024;12:25151355241305856. 39691280 10.1177/25151355241305856 PMC11650588

66. Jumper J Evans R Pritzel A Green T Figurnov M Ronneberger O Highly accurate protein structure prediction with AlphaFold Nature 2021 596 7873 583 9 10.1038/s41586-021-03819-2 34265844 PMC8371605 Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. 34265844 10.1038/s41586-021-03819-2 PMC8371605

67. Anurogo D Luthfiana D Anripa N Fauziah AI Soleha M Rahmah L The art of Bioimmunogenomics (BIGs) 5.0 in CAR-T cell therapy for lymphoma management Adv Pharm Bull 2024 14 2 314 10.34172/apb.2024.034 39206402 PMC11347730 Anurogo D, Luthfiana D, Anripa N, Fauziah AI, Soleha M, Rahmah L, et al. The art of Bioimmunogenomics (BIGs) 5.0 in CAR-T cell therapy for lymphoma management. Adv Pharm Bull. 2024;14(2):314. 39206402 10.34172/apb.2024.034 PMC11347730

68. Barboy O Katzenelenbogen Y Shalita R Amit I In synergy: optimizing CAR T development and personalizing patient care using single-cell technologies Cancer Discov 2023 13 7 1546 55 10.1158/2159-8290.CD-23-0010 37219074 Barboy O, Katzenelenbogen Y, Shalita R, Amit I. In synergy: optimizing CAR T development and personalizing patient care using single-cell technologies. Cancer Discov. 2023;13(7):1546–55. 37219074 10.1158/2159-8290.CD-23-0010

69. Kirouac DC Zmurchok C Deyati A Sicherman J Bond C Zandstra PW Deconvolution of clinical variance in CAR-T cell pharmacology and response Nat Biotechnol 2023 41 11 1606 17 10.1038/s41587-023-01687-x 36849828 PMC10635825 Kirouac DC, Zmurchok C, Deyati A, Sicherman J, Bond C, Zandstra PW. Deconvolution of clinical variance in CAR-T cell pharmacology and response. Nat Biotechnol. 2023;41(11):1606–17. 36849828 10.1038/s41587-023-01687-x PMC10635825

70. Daher M Rezvani K Outlook for new CAR-based therapies with a focus on CAR NK cells: what lies beyond CAR-engineered T cells in the race against cancer Cancer Discov 2021 11 1 45 58 10.1158/2159-8290.CD-20-0556 33277313 PMC8137521 Daher M, Rezvani K. Outlook for new CAR-based therapies with a focus on CAR NK cells: what lies beyond CAR-engineered T cells in the race against cancer. Cancer Discov. 2021;11(1):45–58. 33277313 10.1158/2159-8290.CD-20-0556 PMC8137521

71. Daei Sorkhabi A Mohamed Khosroshahi L Sarkesh A Mardi A Aghebati-Maleki A Aghebati-Maleki L The current landscape of CAR T-cell therapy for solid tumors: mechanisms, research progress, challenges, and counterstrategies Front Immunol 2023 14 1113882 10.3389/fimmu.2023.1113882 37020537 PMC10067596 Daei Sorkhabi A, Mohamed Khosroshahi L, Sarkesh A, Mardi A, Aghebati-Maleki A, Aghebati-Maleki L, et al. The current landscape of CAR T-cell therapy for solid tumors: mechanisms, research progress, challenges, and counterstrategies. Front Immunol. 2023;14:1113882. 37020537 10.3389/fimmu.2023.1113882 PMC10067596

72. Good Z Spiegel JY Sahaf B Malipatlolla MB Ehlinger ZJ Kurra S Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy Nat Med 2022 28 9 1860 71 10.1038/s41591-022-01960-7 36097223 PMC10917089 Good Z, Spiegel JY, Sahaf B, Malipatlolla MB, Ehlinger ZJ, Kurra S, et al. Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy. Nat Med. 2022;28(9):1860–71. 36097223 10.1038/s41591-022-01960-7 PMC10917089

73. Yang C Nguyen J Yen Y Complete spectrum of adverse events associated with chimeric antigen receptor (CAR)-T cell therapies J Biomed Sci 2023 30 1 89 10.1186/s12929-023-00982-8 37864230 PMC10590030 Yang C, Nguyen J, Yen Y. Complete spectrum of adverse events associated with chimeric antigen receptor (CAR)-T cell therapies. J Biomed Sci. 2023;30(1):89. 37864230 10.1186/s12929-023-00982-8 PMC10590030

74. Wei L El Naqa I Artificial intelligence for response evaluation with PET/CT Semin Nucl Med 2021 51 2 157 69 10.1053/j.semnuclmed.2020.10.003 33509372 PMC8099153 Wei L, El Naqa I. Artificial intelligence for response evaluation with PET/CT. Semin Nucl Med. 2021;51(2):157–69. 33509372 10.1053/j.semnuclmed.2020.10.003 PMC8099153

75. Boretti A The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy Comput Biol Med 2024 182 109137 10.1016/j.compbiomed.2024.109137 39260044 Boretti A. The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy. Comput Biol Med. 2024;182:109137. 39260044 10.1016/j.compbiomed.2024.109137

76. Zeng J Lin Z Zhang X Zheng T Xu H Liu T Leveraging artificial intelligence for neoantigen prediction Cancer Res 2025 10.1158/0008-5472.CAN-24-2553 40101113 Zeng J, Lin Z, Zhang X, Zheng T, Xu H, Liu T. Leveraging artificial intelligence for neoantigen prediction. Cancer Res. 2025. 10.1158/0008-5472.CAN-24-2553. 40101113 10.1158/0008-5472.CAN-24-2553

77. Carri I, Schwab E, Podaza E, Alvarez HMG, Mordoh J, Nielsen M et al. Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients. Explor Immunol.3(2):82–103.

78. Nielsen M Andreatta M Peters B Buus S Immunoinformatics: predicting peptide–MHC binding Annu Rev Biomed Data Sci 2020 3 1 191 215 10.1146/annurev-biodatasci-021920-100259 37427310 PMC10328453 Nielsen M, Andreatta M, Peters B, Buus S. Immunoinformatics: predicting peptide–MHC binding. Annu Rev Biomed Data Sci. 2020;3(1):191–215. 37427310 10.1146/annurev-biodatasci-021920-100259 PMC10328453

79. Weber D Ibn-Salem J Sorn P Suchan M Holtsträter C Lahrmann U Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens Nat Biotechnol 2022 40 8 1276 84 10.1038/s41587-022-01247-9 35379963 PMC7613288 Weber D, Ibn-Salem J, Sorn P, Suchan M, Holtsträter C, Lahrmann U, et al. Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens. Nat Biotechnol. 2022;40(8):1276–84. 35379963 10.1038/s41587-022-01247-9 PMC7613288

80. Pounraj S Chen S Ma L Mazzieri R Dolcetti R Rehm BH Targeting tumor heterogeneity with neoantigen-based cancer vaccines Cancer Res 2024 84 3 353 63 10.1158/0008-5472.CAN-23-2042 38055891 Pounraj S, Chen S, Ma L, Mazzieri R, Dolcetti R, Rehm BH. Targeting tumor heterogeneity with neoantigen-based cancer vaccines. Cancer Res. 2024;84(3):353–63. 38055891 10.1158/0008-5472.CAN-23-2042

81. Huber F Arnaud M Stevenson BJ Michaux J Benedetti F Thevenet J A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy Nat Biotechnol 2025 10.1038/s41587-024-02420-y 39394480 PMC12339364 Huber F, Arnaud M, Stevenson BJ, Michaux J, Benedetti F, Thevenet J, et al. A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nat Biotechnol. 2025. 10.1038/s41587-024-02420-y. 39394480 10.1038/s41587-024-02420-y PMC12339364

82. Baker TC. Improving detection and quantification of major histocompatibility complex (MHC)-presented immunopeptides for vaccine development.

83. Greener JG Kandathil SM Moffat L Jones DT A guide to machine learning for biologists Nat Rev Mol Cell Biol 2022 23 1 40 55 10.1038/s41580-021-00407-0 34518686 Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55. 34518686 10.1038/s41580-021-00407-0

84. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res.48(W1):449–54. 10.1093/nar/gkaa379 PMC7319546 32406916

85. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S et al. NetMHCpan is a method for quantitative predictions of peptide binding to any HLA-A and- B locus protein of known sequence. PLoS ONE.2(8):796&#8211. 10.1371/journal.pone.0000796 PMC1949492 17726526

86. O’Donnell TJ, Rubinsteyn A, Laserson U. MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing. Cell Syst.11(1):42–8. 10.1016/j.cels.2020.06.010 32711842

87. Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR et al. HLA-II immunopeptidome profiling and deep learning reveal antigenic features to inform antigen discovery. Immunity.56(7):1681–98. 10.1016/j.immuni.2023.05.009 PMC10519123 37301199

88. Xu S Wang X Fei C A highly effective system for predicting MHC-II epitopes with immunogenicity Front Oncol 2022 12 888556 10.3389/fonc.2022.888556 35785204 PMC9246415 Xu S, Wang X, Fei C. A highly effective system for predicting MHC-II epitopes with immunogenicity. Front Oncol. 2022;12:888556. 35785204 10.3389/fonc.2022.888556 PMC9246415

89. Nilsson JB, Kaabinejadian S, Yari H, Kester MG, Balen P, Hildebrand WH et al. Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning. Sci Adv.9(47):6367&#8211. 10.1126/sciadv.adj6367 PMC10672173 38000035

90. Reynisson B Barra C Kaabinejadian S Hildebrand WH Peters B Nielsen M Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data J Proteome Res 2020 19 6 2304 15 10.1021/acs.jproteome.9b00874 32308001 Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. J Proteome Res. 2020;19(6):2304–15. 32308001 10.1021/acs.jproteome.9b00874

91. Bradley P Structure-based prediction of T cell receptor: peptide-MHC interactions Elife 2023 12 82813– 10.7554/eLife.82813 PMC9859041 36661395 Bradley P. Structure-based prediction of T cell receptor: peptide-MHC interactions. Elife. 2023;12:82813–. 10.7554/eLife.82813 PMC9859041 36661395

92. Ghoreyshi ZS George JT Quantitative approaches for decoding the specificity of the human T cell repertoire Front Immunol 2023 14 1228873– 10.3389/fimmu.2023.1228873 37781387 PMC10539903 Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol. 2023;14:1228873–. 37781387 10.3389/fimmu.2023.1228873 PMC10539903

93. Bulashevska A Nacsa Z Lang F Braun M Machyna M Diken M Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy Front Immunol 2024 15 1394003– 10.3389/fimmu.2024.1394003 38868767 PMC11167095 Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, et al. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol. 2024;15:1394003–. 38868767 10.3389/fimmu.2024.1394003 PMC11167095

94. Kathuria KR Chen B Khodadoust MS Olsson N Davis MM Elias JE Maria-I: a deep-learning approach for accurate prediction of MHC class I tumor neoantigen presentation Blood 2019 134 84– 10.1182/blood-2019-129334 Kathuria KR, Chen B, Khodadoust MS, Olsson N, Davis MM, Elias JE, et al. Maria-I: a deep-learning approach for accurate prediction of MHC class I tumor neoantigen presentation. Blood. 2019;134:84–.

95. Parkhurst MR Robbins PF Tran E Prickett TD Gartner JJ Jia L Unique neoantigens arise from somatic mutations in patients with gastrointestinal cancers Cancer Discov 2019 9 8 1022 35 10.1158/2159-8290.CD-18-1494 31164343 PMC7138461 Parkhurst MR, Robbins PF, Tran E, Prickett TD, Gartner JJ, Jia L, et al. Unique neoantigens arise from somatic mutations in patients with gastrointestinal cancers. Cancer Discov. 2019;9(8):1022–35. 31164343 10.1158/2159-8290.CD-18-1494 PMC7138461

96. Powderly JD, Sullivan RJ, Gutierrez M, Khattak A, Thomas SS, Jimeno A et al. Phase 1/2 study of mRNA-4359 administered alone and in combination with immune checkpoint blockade in adult participants with advanced solid tumors.

97. Xu Y. Deep neural networks for QSAR. Artificial Intelligence in Drug Design. New York, NY: Springer US. pp. 233–60.

98. Bahrami Y Bolideei M Mohammadzadeh S Gahrouei RB Mohebbi E Haider KH Applications of artificial intelligence and nanotechnology in vaccine development Int J Pharm 2025 684 126096 10.1016/j.ijpharm.2025.126096 40886810 Bahrami Y, Bolideei M, Mohammadzadeh S, Gahrouei RB, Mohebbi E, Haider KH, et al. Applications of artificial intelligence and nanotechnology in vaccine development. Int J Pharm. 2025;684:126096. 40886810 10.1016/j.ijpharm.2025.126096

99. Rojas LA Sethna Z Soares KC Olcese C Pang N Patterson E Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer Nature 2023 618 7963 144 50 10.1038/s41586-023-06063-y 37165196 PMC10171177 Rojas LA, Sethna Z, Soares KC, Olcese C, Pang N, Patterson E, et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature. 2023;618(7963):144–50. 37165196 10.1038/s41586-023-06063-y PMC10171177

100. Lin YJ Zimmermann J Schülke S Novel adjuvants in allergen-specific immunotherapy: where do we stand?> Front Immunol 2024 15 1348305 10.3389/fimmu.2024.1348305 38464539 PMC10920236 Lin YJ, Zimmermann J, Schülke S. Novel adjuvants in allergen-specific immunotherapy: where do we stand? Front Immunol. 2024;15:1348305. 38464539 10.3389/fimmu.2024.1348305 PMC10920236

101. Haider S Barakat A Ul-Haq Z Discovery of potential chemical probe as inhibitors of CXCL12 using ligand-based virtual screening and molecular dynamic simulation Molecules 2020 25 20 4829 10.3390/molecules25204829 33092204 PMC7594044 Haider S, Barakat A, Ul-Haq Z. Discovery of potential chemical probe as inhibitors of CXCL12 using ligand-based virtual screening and molecular dynamic simulation. Molecules. 2020;25(20):4829. 33092204 10.3390/molecules25204829 PMC7594044

102. Zhang WY Zheng XL Coghi PS Chen JH Dong BJ Fan XX Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines Front Immunol 2024 15 1438030 10.3389/fimmu.2024.1438030 39206192 PMC11349682 Zhang WY, Zheng XL, Coghi PS, Chen JH, Dong BJ, Fan XX. Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines. Front Immunol. 2024;15:1438030. 39206192 10.3389/fimmu.2024.1438030 PMC11349682

103. Vandenberghe ME Scott ML Scorer PW Söderberg M Balcerzak D Barker C Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer Sci Rep 2017 7 1 45938 10.1038/srep45938 28378829 PMC5380996 Vandenberghe ME, Scott ML, Scorer PW, Söderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep. 2017;7(1):45938. 28378829 10.1038/srep45938 PMC5380996

104. Palm C Connolly CE Masser R Padberg Sgier B Karamitopoulou E Simon Q Determining HER2 status by artificial intelligence: an investigation of primary, metastatic, and HER2 low breast tumors Diagnostics Basel 2023 13 1 168 10.3390/diagnostics13010168 36611460 PMC9818571 Palm C, Connolly CE, Masser R, Padberg Sgier B, Karamitopoulou E, Simon Q, et al. Determining HER2 status by artificial intelligence: an investigation of primary, metastatic, and HER2 low breast tumors. Diagnostics Basel. 2023;13(1):168. 36611460 10.3390/diagnostics13010168 PMC9818571

105. Potočnik J Foley S Thomas E Current and potential applications of artificial intelligence in medical imaging practice: a narrative review J Med Imaging Radiat Sci 2023 54 2 376 85 10.1016/j.jmir.2023.03.033 37062603 Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: a narrative review. J Med Imaging Radiat Sci. 2023;54(2):376–85. 37062603 10.1016/j.jmir.2023.03.033

106. Geis JR Brady AP Wu CC Spencer J Ranschaert E Jaremko JL Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement Radiology 2019 293 2 436 40 10.1148/radiol.2019191586 31573399 Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019;293(2):436–40. 31573399 10.1148/radiol.2019191586

107. Gong J Bao X Wang T Liu J Peng W Shi J A short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer Oncoimmunology 2022 11 1 2028962 10.1080/2162402X.2022.2028962 35096486 PMC8794258 Gong J, Bao X, Wang T, Liu J, Peng W, Shi J, et al. A short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer. Oncoimmunology. 2022;11(1):2028962. 35096486 10.1080/2162402X.2022.2028962 PMC8794258

108. He B Dong D She Y Zhou C Fang M Zhu Y Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker J Immunother Cancer 2020 8 2 550 10.1136/jitc-2020-000550 PMC7342823 32636239 He B, Dong D, She Y, Zhou C, Fang M, Zhu Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer. 2020;8(2):550. 10.1136/jitc-2020-000550 PMC7342823 32636239

109. Tosta TAA, Faria PR, Neves LA, Nascimento MZ. Color normalization of faded H&E-stained histological images using spectral matching. Comput Biol Med.111:103344&#8211. 10.1016/j.compbiomed.2019.103344 31279982

110. Vijh S, Saraswat M, Kumar S. A new complete color normalization method for H&E-stained histopathological images. Appl Intell.51(11):7735–48.

111. Sirinukunwattana K Raza SEA Tsang YW Snead DR Cree IA Rajpoot NM Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images IEEE Trans Med Imaging 2016 35 5 1196 206 10.1109/TMI.2016.2525803 26863654 Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196–206. 26863654 10.1109/TMI.2016.2525803

112. Altini N Brunetti A Puro E Taccogna MG Saponaro C Zito FA Ndg-cam: Nuclei detection in histopathology images with semantic segmentation networks and grad-cam Bioengineering 2022 9 9 475 10.3390/bioengineering9090475 36135021 PMC9495364 Altini N, Brunetti A, Puro E, Taccogna MG, Saponaro C, Zito FA, et al. Ndg-cam: Nuclei detection in histopathology images with semantic segmentation networks and grad-cam. Bioengineering. 2022;9(9):475. 36135021 10.3390/bioengineering9090475 PMC9495364

113. Chang L, Liu J, Zhu J, Guo S, Wang Y, Zhou Z et al. Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization. Cancer Biology Med.22(1):33–47. 10.20892/j.issn.2095-3941.2024.0376 PMC11795263 39749734

114. Tao W Sun Q Xu B Wang R Towards the prediction of responses to cancer immunotherapy: a multi-omics review Life 2025 15 2 283 10.3390/life15020283 40003691 PMC11856636 Tao W, Sun Q, Xu B, Wang R. Towards the prediction of responses to cancer immunotherapy: a multi-omics review. Life. 2025;15(2):283. 40003691 10.3390/life15020283 PMC11856636

115. Kong J Ha D Lee J Kim I Park M Im SH Network-based machine learning approach to predict immunotherapy response in cancer patients Nat Commun 2022 13 1 3703 10.1038/s41467-022-31535-6 35764641 PMC9240063 Kong J, Ha D, Lee J, Kim I, Park M, Im SH, et al. Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun. 2022;13(1):3703. 35764641 10.1038/s41467-022-31535-6 PMC9240063

116. Li S, Li W, Ma T, Fu S, Gao X, Qin N et al. Assessing the efficacy of immunotherapy in lung squamous carcinoma using an artificial intelligence neural network. Front Immunol.13:1024707&#8211. 10.3389/fimmu.2022.1024707 PMC9742243 36518765

117. Kather JN Pearson AT Halama N Jäger D Krause J Loosen SH Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer Nat Med 2019 25 7 1054 6 10.1038/s41591-019-0462-y 31160815 PMC7423299 Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054–6. 31160815 10.1038/s41591-019-0462-y PMC7423299

118. Tapper W Carneiro G Mikropoulos C Thomas SA Evans PM Boussios S The application of radiomics and AI to molecular imaging for prostate cancer J Pers Med 2024 14 3 287 10.3390/jpm14030287 38541029 PMC10971024 Tapper W, Carneiro G, Mikropoulos C, Thomas SA, Evans PM, Boussios S. The application of radiomics and AI to molecular imaging for prostate cancer. J Pers Med. 2024;14(3):287. 38541029 10.3390/jpm14030287 PMC10971024

119. Ozaki Y Broughton P Abdollahi H Valafar H Blenda AV Integrating omics data and AI for cancer diagnosis and prognosis Cancers 2024 16 13 2448 10.3390/cancers16132448 39001510 PMC11240413 Ozaki Y, Broughton P, Abdollahi H, Valafar H, Blenda AV. Integrating omics data and AI for cancer diagnosis and prognosis. Cancers. 2024;16(13):2448. 39001510 10.3390/cancers16132448 PMC11240413

120. Casak SJ Marcus L Fashoyin-Aje L Mushti SL Cheng J Shen YL FDA approval summary: pembrolizumab for the first-line treatment of patients with MSI-H/dMMR advanced unresectable or metastatic colorectal carcinoma Clin Cancer Res 2021 27 17 4680 4 10.1158/1078-0432.CCR-21-0557 33846198 PMC8416693 Casak SJ, Marcus L, Fashoyin-Aje L, Mushti SL, Cheng J, Shen YL, et al. FDA approval summary: pembrolizumab for the first-line treatment of patients with MSI-H/dMMR advanced unresectable or metastatic colorectal carcinoma. Clin Cancer Res. 2021;27(17):4680–4. 33846198 10.1158/1078-0432.CCR-21-0557 PMC8416693

121. Pembrolizumab for previously. treated, microsatellite instability–high/mismatch repair–deficient advanced colorectal cancer: final analysis of KEYNOTE-164. Eur J Cancer.186:185–95. 10.1016/j.ejca.2023.02.016 37141828

122. Priestley P Baber J Lolkema MP Steeghs N Bruijn E Shale C Pan-cancer whole-genome analyses of metastatic solid tumours Nature 2019 575 7781 210 6 10.1038/s41586-019-1689-y 31645765 PMC6872491 Priestley P, Baber J, Lolkema MP, Steeghs N, Bruijn E, Shale C, et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature. 2019;575(7781):210–6. 31645765 10.1038/s41586-019-1689-y PMC6872491

123. Bao X, Zhang H, Wu W, Cheng S, Dai X, Zhu X et al. Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy—Journal for immunotherapy of cancer. p. 1437&#8211. 10.1136/jitc-2020-001437 PMC7542666 33028695

124. Lu C Zhang YC Chen ZH Zhou Q Wu YL Applications of circulating tumor DNA in immune checkpoint inhibition: emerging roles and future perspectives Front Oncol 2022 12 836891 10.3389/fonc.2022.836891 35359372 PMC8963952 Lu C, Zhang YC, Chen ZH, Zhou Q, Wu YL. Applications of circulating tumor DNA in immune checkpoint inhibition: emerging roles and future perspectives. Front Oncol. 2022;12:836891. 35359372 10.3389/fonc.2022.836891 PMC8963952

125. Saillard C Dubois R Tchita O Loiseau N Garcia T Adriansen A Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides Nat Commun 2023 14 1 6695 10.1038/s41467-023-42453-6 37932267 PMC10628260 Saillard C, Dubois R, Tchita O, Loiseau N, Garcia T, Adriansen A, et al. Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides. Nat Commun. 2023;14(1):6695. 37932267 10.1038/s41467-023-42453-6 PMC10628260

126. Koelzer VH, Gisler A, Hanhart JC, Griss J, Wagner SN, Willi N et al. Digital image analysis enhances the precision of PD-L1 scoring in cutaneous melanoma. Histopathology.73(3):397–406. 10.1111/his.13528 29660160

127. Schmitt AM Larkin J Patel SP Dual immune checkpoint inhibition in melanoma and PD-L1 expression: the jury is still out J Clin Oncol 2025 43 2 122 4 10.1200/JCO-24-01572 39374477 Schmitt AM, Larkin J, Patel SP. Dual immune checkpoint inhibition in melanoma and PD-L1 expression: the jury is still out. J Clin Oncol. 2025;43(2):122–4. 39374477 10.1200/JCO-24-01572

128. Wu J Liu C Liu X Sun W Li L Gao N Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer Mod Pathol 2022 35 3 403 11 10.1038/s41379-021-00904-9 34518630 Wu J, Liu C, Liu X, Sun W, Li L, Gao N, et al. Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Mod Pathol. 2022;35(3):403–11. 34518630 10.1038/s41379-021-00904-9

129. Chan TA Yarchoan M Jaffee E Swanton C Quezada SA Stenzinger A Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic Ann Oncol 2019 30 1 44 56 10.1093/annonc/mdy495 30395155 PMC6336005 Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30(1):44–56. 30395155 10.1093/annonc/mdy495 PMC6336005

130. Moeckel C Bakhl K Georgakopoulos-Soares I Zaravinos A The efficacy of tumor mutation burden as a biomarker of response to immune checkpoint inhibitors Int J Mol Sci 2023 24 7 6710 10.3390/ijms24076710 37047684 PMC10095310 Moeckel C, Bakhl K, Georgakopoulos-Soares I, Zaravinos A. The efficacy of tumor mutation burden as a biomarker of response to immune checkpoint inhibitors. Int J Mol Sci. 2023;24(7):6710. 37047684 10.3390/ijms24076710 PMC10095310

131. Jain MS Massoud TF Predicting tumour mutational burden from histopathological images using multiscale deep learning Nat Mach Intell 2020 2 6 356 62 10.1038/s42256-020-0190-5 Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat Mach Intell. 2020;2(6):356–62.

132. Dewaker V, Morya VK, Kim YH, Park ST, Kim HS, Koh YH. Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design and optimization tool. Biomark Res.13(1):52&#8211. 10.1186/s40364-025-00764-4 PMC11954232 40155973

133. Cheng J Liang T Xie XQ Feng Z Meng L A new era of antibody discovery: an in-depth review of AI-driven approaches Drug Discov Today 2024 10.1016/j.drudis.2024.103984 38642702 Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today. 2024. 10.1016/j.drudis.2024.103984. 38642702 10.1016/j.drudis.2024.103984

134. Zhang DE He T Shi T Huang K Peng A Trends in the research and development of peptide drug conjugates: Artificial Intelligence-aided design Front Pharmacol 2025 16 1553853 10.3389/fphar.2025.1553853 40083376 PMC11903715 Zhang DE, He T, Shi T, Huang K, Peng A. Trends in the research and development of peptide drug conjugates: Artificial Intelligence-aided design. Front Pharmacol. 2025;16:1553853. 40083376 10.3389/fphar.2025.1553853 PMC11903715

135. Zhang Y Luo M Wu P Wu S Lee T-Y Bai C Application of computational biology and artificial intelligence in drug design Int J Mol Sci 2022 23 21 13568 10.3390/ijms232113568 36362355 PMC9658956 Zhang Y, Luo M, Wu P, Wu S, Lee T-Y, Bai C. Application of computational biology and artificial intelligence in drug design. Int J Mol Sci. 2022;23(21):13568. 36362355 10.3390/ijms232113568 PMC9658956

136. Georgiou G Ippolito GC Beausang J Busse CE Wardemann H Quake SR The promise and challenge of high-throughput sequencing of the antibody repertoire Nat Biotechnol 2014 32 2 158 68 10.1038/nbt.2782 24441474 PMC4113560 Georgiou G, Ippolito GC, Beausang J, Busse CE, Wardemann H, Quake SR. The promise and challenge of high-throughput sequencing of the antibody repertoire. Nat Biotechnol. 2014;32(2):158–68. 24441474 10.1038/nbt.2782 PMC4113560

137. Liu G Zeng H Mueller J Carter B Wang Z Schilz J Antibody complementarity-determining region design using high-capacity machine learning Bioinformatics 2020 36 7 2126 33 10.1093/bioinformatics/btz895 31778140 PMC7141872 Liu G, Zeng H, Mueller J, Carter B, Wang Z, Schilz J, et al. Antibody complementarity-determining region design using high-capacity machine learning. Bioinformatics. 2020;36(7):2126–33. 31778140 10.1093/bioinformatics/btz895 PMC7141872

138. Mason DM Friedensohn S Weber CR Jordi C Wagner B Meng SM Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning Nat Biomed Eng 2021 5 6 600 12 10.1038/s41551-021-00699-9 33859386 Mason DM, Friedensohn S, Weber CR, Jordi C, Wagner B, Meng SM, et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat Biomed Eng. 2021;5(6):600–12. 33859386 10.1038/s41551-021-00699-9

139. Makowski EK, Kinnunen PC, Huang J, Wu L, Smith MD, Wang T et al. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space. Nat Commun.13(1):3788&#8211. 10.1038/s41467-022-31457-3 PMC9249733 35778381

140. Saka K, Kakuzaki T, Metsugi S, Kashiwagi D, Yoshida K, Wada M et al. Antibody design using an LSTM-based deep generative model from the phage display library for affinity maturation. Sci Rep.11(1):5852&#8211. 10.1038/s41598-021-85274-7 PMC7955064 33712669

141. Baek M DiMaio F Anishchenko I Dauparas J Ovchinnikov S Lee GR Accurate prediction of protein structures and interactions using a three-track neural network Science 2021 373 6557 871 6 10.1126/science.abj8754 34282049 PMC7612213 Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871–6. 34282049 10.1126/science.abj8754 PMC7612213

142. Xu J Distance-based protein folding powered by deep learning Proc Natl Acad Sci U S A 2019 116 34 16856 65 10.1073/pnas.1821309116 31399549 PMC6708335 Xu J. Distance-based protein folding powered by deep learning. Proc Natl Acad Sci U S A. 2019;116(34):16856–65. 31399549 10.1073/pnas.1821309116 PMC6708335

143. He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.

144. Ruffolo JA Sulam J Gray JJ Antibody structure prediction using interpretable deep learning Patterns 2022 10.1016/j.patter.2021.100406 35199061 PMC8848015 Ruffolo JA, Sulam J, Gray JJ. Antibody structure prediction using interpretable deep learning. Patterns. 2022. 10.1016/j.patter.2021.100406. 35199061 10.1016/j.patter.2021.100406 PMC8848015

145. Raybould MI Marks C Krawczyk K Taddese B Nowak J Lewis AP Five computational developability guidelines for therapeutic antibody profiling Proc Natl Acad Sci U S A 2019 116 10 4025 30 10.1073/pnas.1810576116 30765520 PMC6410772 Raybould MI, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, et al. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci U S A. 2019;116(10):4025–30. 30765520 10.1073/pnas.1810576116 PMC6410772

146. Abanades B Georges G Bujotzek A Deane CM ABlooper: fast, accurate antibody CDR loop structure prediction with accuracy estimation Bioinformatics 2022 38 7 1877 80 10.1093/bioinformatics/btac016 35099535 PMC8963302 Abanades B, Georges G, Bujotzek A, Deane CM. ABlooper: fast, accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics. 2022;38(7):1877–80. 35099535 10.1093/bioinformatics/btac016 PMC8963302

147. Satorras VG, Hoogeboom E, Welling M, editors. E (n) equivariant graph neural networks. International Conference on machine learning: PMLR.

148. Cohen T Halfon M Schneidman-Duhovny D NanoNet: rapid and accurate end-to-end nanobody modeling by deep learning Front Immunol 2022 13 958584 10.3389/fimmu.2022.958584 36032123 PMC9411858 Cohen T, Halfon M, Schneidman-Duhovny D. NanoNet: rapid and accurate end-to-end nanobody modeling by deep learning. Front Immunol. 2022;13:958584. 36032123 10.3389/fimmu.2022.958584 PMC9411858

149. Davila A Xu Z Li S Rozewicki J Wilamowski J Kotelnikov S AbAdapt: an adaptive approach to predicting antibody–antigen complex structures from sequence Bioinform Adv 2022 10.1093/bioadv/vbac015 36699363 PMC9710585 Davila A, Xu Z, Li S, Rozewicki J, Wilamowski J, Kotelnikov S, et al. AbAdapt: an adaptive approach to predicting antibody–antigen complex structures from sequence. Bioinform Adv. 2022. 10.1093/bioadv/vbac015. 36699363 10.1093/bioadv/vbac015 PMC9710585

150. Xu Z Davila A Wilamowski J Teraguchi S Standley DM Improved antibody-specific epitope prediction using alphafold and abadapt Chembiochem 2022 23 18 202200303 10.1002/cbic.202200303 PMC9543094 35893479 Xu Z, Davila A, Wilamowski J, Teraguchi S, Standley DM. Improved antibody-specific epitope prediction using alphafold and abadapt. Chembiochem. 2022;23(18):202200303. 10.1002/cbic.202200303 PMC9543094 35893479

151. Jarasch A Koll H Regula JT Bader M Papadimitriou A Kettenberger H Developability assessment during the selection of novel therapeutic antibodies J Pharm Sci 2015 104 6 1885 98 10.1002/jps.24430 25821140 Jarasch A, Koll H, Regula JT, Bader M, Papadimitriou A, Kettenberger H. Developability assessment during the selection of novel therapeutic antibodies. J Pharm Sci. 2015;104(6):1885–98. 25821140 10.1002/jps.24430

152. Xu Y Wang D Mason B Rossomando T Li N Liu D Structure, heterogeneity, and developability assessment of therapeutic antibodies MAbs 2019 11 2 239 64 10.1080/19420862.2018.1553476 30543482 PMC6380400 Xu Y, Wang D, Mason B, Rossomando T, Li N, Liu D, et al. Structure, heterogeneity, and developability assessment of therapeutic antibodies. MAbs. 2019;11(2):239–64. 30543482 10.1080/19420862.2018.1553476 PMC6380400

153. Wollacott AM Xue C Qin Q Hua J Bohnuud T Viswanathan K Quantifying the nativeness of antibody sequences using long short-term memory networks Protein Eng Des Sel 2019 32 7 347 54 10.1093/protein/gzz031 31504835 PMC7372931 Wollacott AM, Xue C, Qin Q, Hua J, Bohnuud T, Viswanathan K, et al. Quantifying the nativeness of antibody sequences using long short-term memory networks. Protein Eng Des Sel. 2019;32(7):347–54. 31504835 10.1093/protein/gzz031 PMC7372931

154. Prihoda D Maamary J Waight A Juan V Fayadat-Dilman L Svozil D BioPhi: a platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning MAbs 2022 14 1 2020203 10.1080/19420862.2021.2020203 35133949 PMC8837241 Prihoda D, Maamary J, Waight A, Juan V, Fayadat-Dilman L, Svozil D, et al. BioPhi: a platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. MAbs. 2022;14(1):2020203. 35133949 10.1080/19420862.2021.2020203 PMC8837241

155. Lai PK Deepscm: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity Comput Struct Biotechnol J 2022 20 2143 52 10.1016/j.csbj.2022.04.035 35832619 PMC9092385 Lai PK. Deepscm: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity. Comput Struct Biotechnol J. 2022;20:2143–52. 35832619 10.1016/j.csbj.2022.04.035 PMC9092385

156. Agrawal NJ Helk B Kumar S Mody N Sathish HA Samra HS Computational tool for the early screening of monoclonal antibodies for their viscosities MAbs 2016 8 1 43 8 10.1080/19420862.2015.1099773 26399600 PMC4966561 Agrawal NJ, Helk B, Kumar S, Mody N, Sathish HA, Samra HS, et al. Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs. 2016;8(1):43–8. 26399600 10.1080/19420862.2015.1099773 PMC4966561

157. Wu H Pfarr DS Johnson S Brewah YA Woods RM Patel NK Development of motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract J Mol Biol 2007 368 3 652 65 10.1016/j.jmb.2007.02.024 17362988 Wu H, Pfarr DS, Johnson S, Brewah YA, Woods RM, Patel NK, et al. Development of motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract. J Mol Biol. 2007;368(3):652–65. 17362988 10.1016/j.jmb.2007.02.024

158. Kelly RL Yu Y Sun T Caffry I Lynaugh H Brown M Target-independent variable region-mediated effects on antibody clearance can be FcRn-independent MAbs 2016 8 7 1269 75 10.1080/19420862.2016.1208330 27610650 PMC5058615 Kelly RL, Yu Y, Sun T, Caffry I, Lynaugh H, Brown M, et al. Target-independent variable region-mediated effects on antibody clearance can be FcRn-independent. MAbs. 2016;8(7):1269–75. 27610650 10.1080/19420862.2016.1208330 PMC5058615

159. Grinshpun B Thorsteinson N Pereira JN Rippmann F Nannemann D Sood VD Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies MAbs 2021 13 1 1932230 10.1080/19420862.2021.1932230 34116620 PMC8204999 Grinshpun B, Thorsteinson N, Pereira JN, Rippmann F, Nannemann D, Sood VD, et al. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. MAbs. 2021;13(1):1932230. 34116620 10.1080/19420862.2021.1932230 PMC8204999

160. Kumar S, Kaushik D, Sharma SK. Autoimmune disorders: Types, symptoms, and risk factors. Artificial intelligence and autoimmune diseases: applications in the diagnosis, prognosis, and therapeutics. pp. 3–31.

161. Mane DV Deshmukh AN Ambare RH Solankar AA Madane CS AI in autoimmune diseases: transforming diagnosis and treatment J Pharm Biol Sci 2025 12 2 109 18 10.18231/j.jpbs.2024.017 Mane DV, Deshmukh AN, Ambare RH, Solankar AA, Madane CS. AI in autoimmune diseases: transforming diagnosis and treatment. J Pharm Biol Sci. 2025;12(2):109–18.

162. Laigle L Chadli L Moingeon P Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises Expert Rev Clin Immunol 2023 19 3 305 14 10.1080/1744666X.2023.2172404 36680799 Laigle L, Chadli L, Moingeon P. Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises. Expert Rev Clin Immunol. 2023;19(3):305–14. 36680799 10.1080/1744666X.2023.2172404

163. Jorge A Castro VM Barnado A Gainer V Hong C Cai T Identifying lupus patients in electronic health records: development and validation of machine learning algorithms and application of rule-based algorithms Semin Arthritis Rheum 2019 49 1 84 90 10.1016/j.semarthrit.2019.01.002 30665626 PMC6609504 Jorge A, Castro VM, Barnado A, Gainer V, Hong C, Cai T, et al. Identifying lupus patients in electronic health records: development and validation of machine learning algorithms and application of rule-based algorithms. Semin Arthritis Rheum. 2019;49(1):84–90. 30665626 10.1016/j.semarthrit.2019.01.002 PMC6609504

164. Sorkhabi MA Potapenko IO Ilginis T Alberti M Cabrerizo J Assessment of anterior uveitis through anterior-segment optical coherence tomography and artificial intelligence-based image analyses Transl Vis Sci Technol 2022 11 4 7 10.1167/tvst.11.4.7 35394486 PMC8994203 Sorkhabi MA, Potapenko IO, Ilginis T, Alberti M, Cabrerizo J. Assessment of anterior uveitis through anterior-segment optical coherence tomography and artificial intelligence-based image analyses. Transl Vis Sci Technol. 2022;11(4):7. 35394486 10.1167/tvst.11.4.7 PMC8994203

165. Haggag S Khalifa F Abdeltawab H Elnakib A Ghazal M Mohamed MA An automated CAD system for accurate grading of uveitis using optical coherence tomography images Sensors 2021 10.3390/s21165457 34450898 PMC8401645 Haggag S, Khalifa F, Abdeltawab H, Elnakib A, Ghazal M, Mohamed MA, et al. An automated CAD system for accurate grading of uveitis using optical coherence tomography images. Sensors. 2021. 10.3390/s21165457. 34450898 10.3390/s21165457 PMC8401645

166. Liu S Liu Y Li M Shang S Cao Y Shen X Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades Front Immunol 2025 16 1525462 10.3389/fimmu.2025.1525462 40330462 PMC12052778 Liu S, Liu Y, Li M, Shang S, Cao Y, Shen X, et al. Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades. Front Immunol. 2025;16:1525462. 40330462 10.3389/fimmu.2025.1525462 PMC12052778

167. Raza K Singh NK A tour of unsupervised deep learning for medical image analysis Curr Med Imaging Rev 2021 17 9 1059 77 10.2174/18756603MTEzoNzMk0 33504314 Raza K, Singh NK. A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging Rev. 2021;17(9):1059–77. 10.2174/1573405617666210127154257 33504314

168. Wang J Wang S Zhang Y Deep learning on medical image analysis CAAI Trans Intell Technol 2025 10 1 1 35 10.1049/cit2.12356 Wang J, Wang S, Zhang Y. Deep learning on medical image analysis. CAAI Trans Intell Technol. 2025;10(1):1–35.

169. Ahn JC Connell A Simonetto DA Hughes C Shah VH Application of artificial intelligence for the diagnosis and treatment of liver diseases Hepatology 2021 73 6 2546 63 10.1002/hep.31603 33098140 Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology. 2021;73(6):2546–63. 33098140 10.1002/hep.31603

170. Chen H Sung JJ Potentials of AI in medical image analysis in Gastroenterology and Hepatology J Gastroenterol Hepatol 2021 36 1 31 8 10.1111/jgh.15327 33140875 Chen H, Sung JJ. Potentials of AI in medical image analysis in Gastroenterology and Hepatology. J Gastroenterol Hepatol. 2021;36(1):31–8. 33140875 10.1111/jgh.15327

171. Zhuang H, Zhang J, Liao F. A systematic review on the application of deep learning in digestive system image processing. Visual Comput.39(6):2207–22. 10.1007/s00371-021-02322-z PMC8557108 34744231

172. Christou CD Tsoulfas G Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology World J Gastroenterol 2021 27 37 6191 10.3748/wjg.v27.i37.6191 34712027 PMC8515803 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol. 2021;27(37):6191. 34712027 10.3748/wjg.v27.i37.6191 PMC8515803

173. Wu H Yin H Chen H Sun M Liu X Yu Y A deep learning, image based approach for automated diagnosis for inflammatory skin diseases Ann Transl Med 2020 8 9 581 10.21037/atm.2020.04.39 32566608 PMC7290553 Wu H, Yin H, Chen H, Sun M, Liu X, Yu Y, et al. A deep learning, image based approach for automated diagnosis for inflammatory skin diseases. Ann Transl Med. 2020;8(9):581. 32566608 10.21037/atm.2020.04.39 PMC7290553

174. Siddiqui MF, Mouna A, Nicolas G, Rahat SAA, Mitalipova A, Emmanuel N et al. Computational intelligence: a step forward in cancer biomarker discovery and therapeutic target prediction. Computational Intelligence in Oncology: Applications in Diagnosis, Prognosis and Therapeutics of Cancers. Singapore: Springer Singapore. pp. 233–50.

175. Mishra A, Sharma S, Pandey SK. The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment. Recent Patents on Anti-Cancer Drug Discovery. 10.2174/0115748928361472250123105507 39902536

176. Khalifa A Obeid JS Erno J Rockey DC The role of artificial intelligence in hepatology research and practice Curr Opin Gastroenterol 2023 39 3 175 80 10.1097/MOG.0000000000000926 37144534 Khalifa A, Obeid JS, Erno J, Rockey DC. The role of artificial intelligence in hepatology research and practice. Curr Opin Gastroenterol. 2023;39(3):175–80. 37144534 10.1097/MOG.0000000000000926

177. Zhou XQ Huang S Shi XM Liu S Zhang W Shi L Global trends in artificial intelligence applications in liver disease over seventeen years World J Hepatol 2025 17 3 101721 10.4254/wjh.v17.i3.101721 40177211 PMC11959664 Zhou XQ, Huang S, Shi XM, Liu S, Zhang W, Shi L, et al. Global trends in artificial intelligence applications in liver disease over seventeen years. World J Hepatol. 2025;17(3):101721. 40177211 10.4254/wjh.v17.i3.101721 PMC11959664

178. Krittanawong C Zhang H Wang Z Aydar M Kitai T Artificial intelligence in precision cardiovascular medicine J Am Coll Cardiol 2017 69 21 2657 64 10.1016/j.jacc.2017.03.571 28545640 Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64. 28545640 10.1016/j.jacc.2017.03.571

179. Manlhiot C, Eynde J, Kutty S, Ross HJ. A primer on the present state and prospects for machine learning and artificial intelligence applications in cardiology. Can J Cardiol.38(2):169–84. 10.1016/j.cjca.2021.11.009 34838700

180. Labkoff S Oladimeji B Kannry J Solomonides A Leftwich R Koski E Toward a responsible future: recommendations for AI-enabled clinical decision support J Am Med Inform Assoc 2024 31 11 2730 9 10.1093/jamia/ocae209 39325508 PMC11491642 Labkoff S, Oladimeji B, Kannry J, Solomonides A, Leftwich R, Koski E, et al. Toward a responsible future: recommendations for AI-enabled clinical decision support. J Am Med Inform Assoc. 2024;31(11):2730–9. 39325508 10.1093/jamia/ocae209 PMC11491642

181. Karalis VD The integration of artificial intelligence into clinical practice Appl Biosci 2024 3 1 14 44 10.3390/applbiosci3010002 Karalis VD. The integration of artificial intelligence into clinical practice. Appl Biosci. 2024;3(1):14–44.

182. Shaikh F Dehmeshki J Bisdas S Roettger-Dupont D Kubassova O Aziz M Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics Curr Probl Diagn Radiol 2021 50 2 262 7 10.1067/j.cpradiol.2020.05.006 32591104 Shaikh F, Dehmeshki J, Bisdas S, Roettger-Dupont D, Kubassova O, Aziz M, et al. Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Curr Probl Diagn Radiol. 2021;50(2):262–7. 32591104 10.1067/j.cpradiol.2020.05.006

183. Boeken T Feydy J Lecler A Soyer P Feydy A Barat M Artificial intelligence in diagnostic and interventional radiology: where are we now?> Diagn Interv Imaging 2023 104 1 1 5 10.1016/j.diii.2022.11.004 36494290 Boeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, et al. Artificial intelligence in diagnostic and interventional radiology: where are we now? Diagn Interv Imaging. 2023;104(1):1–5. 36494290 10.1016/j.diii.2022.11.004

184. Wong GLH Yuen PC Ma AJ Chan AWH Leung HHW Wong VWS Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis J Gastroenterol Hepatol 2021 36 3 543 50 10.1111/jgh.15385 33709607 Wong GLH, Yuen PC, Ma AJ, Chan AWH, Leung HHW, Wong VWS. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol. 2021;36(3):543–50. 33709607 10.1111/jgh.15385

185. Lu F Meng Y Song X Li X Liu Z Gu C Artificial intelligence in liver diseases: recent advances Adv Ther 2024 41 3 967 90 10.1007/s12325-024-02781-5 38286960 Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, et al. Artificial intelligence in liver diseases: recent advances. Adv Ther. 2024;41(3):967–90. 38286960 10.1007/s12325-024-02781-5

186. Siddiqui MF, Alam A, Kalmatov R, Mouna A, Villela R, Mitalipova A et al. Leveraging healthcare system with nature-inspired computing techniques: an overview and future perspective. Nature-Inspired Intelligent Computing Techniques in Bioinformatics. pp. 19–42.

187. Su TH Wu CH Kao JH Artificial intelligence in precision medicine in hepatology J Gastroenterol Hepatol 2021 36 3 569 80 10.1111/jgh.15415 33709606 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol. 2021;36(3):569–80. 33709606 10.1111/jgh.15415

188. Schattenberg JM Chalasani N Alkhouri N Artificial intelligence applications in hepatology Clin Gastroenterol Hepatol 2023 21 8 2015 25 10.1016/j.cgh.2023.04.007 37088460 Schattenberg JM, Chalasani N, Alkhouri N. Artificial intelligence applications in hepatology. Clin Gastroenterol Hepatol. 2023;21(8):2015–25. 37088460 10.1016/j.cgh.2023.04.007

189. Qazi S, Iqbal N, Raza K. Fuzzy logic-based hybrid models for clinical decision support systems in cancer. Computational Intelligence in Oncology: Applications in Diagnosis, Prognosis and Therapeutics of Cancers. Singapore: Springer Singapore. pp. 201–13.

190. Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology.158(1):76–94. 10.1053/j.gastro.2019.08.058 31593701

191. Calderaro J Žigutytė L Truhn D Jaffe A Kather JN Artificial intelligence in liver cancer—new tools for research and patient management Nat Rev Gastroenterol Hepatol 2024 21 8 585 99 10.1038/s41575-024-00919-y 38627537 Calderaro J, Žigutytė L, Truhn D, Jaffe A, Kather JN. Artificial intelligence in liver cancer—new tools for research and patient management. Nat Rev Gastroenterol Hepatol. 2024;21(8):585–99. 38627537 10.1038/s41575-024-00919-y

192. Rajkomar A Hardt M Howell MD Corrado G Chin MH Ensuring fairness in machine learning to advance health equity Ann Intern Med 2018 169 12 866 72 10.7326/M18-1990 30508424 PMC6594166 Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866–72. 30508424 10.7326/M18-1990 PMC6594166

193. Yang Y Liu Y Chen Y Luo D Xu K Zhang L Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives Front Immunol 2024 15 1477130 10.3389/fimmu.2024.1477130 39502698 PMC11534874 Yang Y, Liu Y, Chen Y, Luo D, Xu K, Zhang L. Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives. Front Immunol. 2024;15:1477130. 39502698 10.3389/fimmu.2024.1477130 PMC11534874

194. Moingeon P Artificial intelligence-driven drug development against autoimmune diseases Trends Pharmacol Sci 2023 44 7 411 24 10.1016/j.tips.2023.04.005 37268540 Moingeon P. Artificial intelligence-driven drug development against autoimmune diseases. Trends Pharmacol Sci. 2023;44(7):411–24. 37268540 10.1016/j.tips.2023.04.005

195. Cheng X Meng X Chen R Song Z Li S Wei S The molecular subtypes of autoimmune diseases Comput Struct Biotechnol J 2024 10.1016/j.csbj.2024.03.026 38596313 PMC11001648 Cheng X, Meng X, Chen R, Song Z, Li S, Wei S, et al. The molecular subtypes of autoimmune diseases. Comput Struct Biotechnol J. 2024. 10.1016/j.csbj.2024.03.026. 38596313 10.1016/j.csbj.2024.03.026 PMC11001648

196. Conte L, Caruso G, Philip AK, Cucci F, Nunzio G, Cascio D et al. Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature. Cancers.17(4):571&#8211. 10.3390/cancers17040571 PMC11852502 40002166

197. Jiang P Sinha S Aldape K Hannenhalli S Sahinalp C Ruppin E Big data in basic and translational cancer research Nat Rev Cancer 2022 22 11 625 39 10.1038/s41568-022-00502-0 36064595 PMC9443637 Jiang P, Sinha S, Aldape K, Hannenhalli S, Sahinalp C, Ruppin E. Big data in basic and translational cancer research. Nat Rev Cancer. 2022;22(11):625–39. 36064595 10.1038/s41568-022-00502-0 PMC9443637

198. Biswas N, Chakrabarti S. Artificial intelligence (AI)-based systems biology approaches in the analysis of multi-omics data for cancer. Front Oncol.10:588221&#8211. 10.3389/fonc.2020.588221 PMC7591760 33154949

199. Rehman AU, Li M, Wu B, Ali Y, Rasheed S, Shaheen S et al. Role of artificial intelligence in revolutionizing drug discovery: Fundamental Research. 10.1016/j.fmre.2024.04.021 PMC12167903 40528990

200. Saini JPS, Thakur A, Yadav D. AI-driven Innovations in Pharmaceuticals: Optimizing Drug Discovery and Industry Operations: RSC Pharmaceutics.

201. Smith DP, Oechsle O, Rawling MJ, Savory E, Lacoste AMB, Richardson PJ. Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19. Front Pharmacol. 2021;Volume 12–2021. 10.3389/fphar.2021.709856 PMC8356560 34393789

202. Richardson P Griffin I Tucker C Smith D Oechsle O Phelan A Baricitinib as potential treatment for 2019-nCoV acute respiratory disease Lancet 2020 395 10223 e30 e1 10.1016/S0140-6736(20)30304-4 32032529 PMC7137985 Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, Phelan A, et al. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet. 2020;395(10223):e30–1. 32032529 10.1016/S0140-6736(20)30304-4 PMC7137985

203. Niarakis A Laubenbacher R An G Ilan Y Fisher J Flobak Å Immune digital twins for complex human pathologies: applications, limitations, and challenges NPJ Syst Biol Appl 2024 10 1 141 10.1038/s41540-024-00450-5 39616158 PMC11608242 Niarakis A, Laubenbacher R, An G, Ilan Y, Fisher J, Flobak Å, et al. Immune digital twins for complex human pathologies: applications, limitations, and challenges. NPJ Syst Biol Appl. 2024;10(1):141. 39616158 10.1038/s41540-024-00450-5 PMC11608242

204. Wang K Parrott NJ Lavé T Embracing the future of medicine with virtual patients Drug Discov Today 2025 10.1016/j.drudis.2025.104322 40032136 Wang K, Parrott NJ, Lavé T. Embracing the future of medicine with virtual patients. Drug Discov Today. 2025. 10.1016/j.drudis.2025.104322. 40032136 10.1016/j.drudis.2025.104322

205. Debnath R, Ikbal AMA, Choudhury A, Mandal SC, Palit P. Artificial Intelligence: A Major Landmark in the Novel Drug Discovery Pathway for the Remarkable Advancement in the Healthcare System. Concepts in Pharmaceutical Biotechnology and Drug Development. Singapore: Springer Nature Singapore. pp. 413–36.

206. Lu MY Chuang WL Yu ML The role of artificial intelligence in the management of liver diseases Kaohsiung J Med Sci 2024 40 11 962 71 10.1002/kjm2.12901 39440678 PMC11895080 Lu MY, Chuang WL, Yu ML. The role of artificial intelligence in the management of liver diseases. Kaohsiung J Med Sci. 2024;40(11):962–71. 39440678 10.1002/kjm2.12901 PMC11895080

207. Gangwal A Lavecchia A Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing Drug Discov Today 2025 10.1016/j.drudis.2025.104360 40252989 Gangwal A, Lavecchia A. Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing. Drug Discov Today. 2025. 10.1016/j.drudis.2025.104360. 40252989 10.1016/j.drudis.2025.104360

208. Sen S, Kilic KD. Digital Twins in Medicine, AI-Driven Personalized Healthcare, and Predictive Analytics. AI-Powered Digital Twins for Predictive Healthcare: Creating Virtual Replicas of Humans: IGI Global Scientific Publishing. pp. 359–96.

209. Wang J Tian Y Zhou T Tong D Ma J Li J A survey of artificial intelligence in rheumatoid arthritis Rheumatology and Immunology Research 2023 4 2 69 77 10.2478/rir-2023-0011 37485476 PMC10362600 Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. Rheumatology and Immunology Research. 2023;4(2):69–77. 37485476 10.2478/rir-2023-0011 PMC10362600

210. Ullah R Sarwar N Alatawi MN Alsadhan AA Salamah Alwageed H Khan M Advancing personalized diagnosis and treatment using deep learning architecture Front Med (Lausanne) 2025 12 1545528 10.3389/fmed.2025.1545528 40212269 PMC11983605 Ullah R, Sarwar N, Alatawi MN, Alsadhan AA, Salamah Alwageed H, Khan M, et al. Advancing personalized diagnosis and treatment using deep learning architecture. Front Med (Lausanne). 2025;12:1545528. 40212269 10.3389/fmed.2025.1545528 PMC11983605

211. Rigi A, Harati K, Abbaspour M, Fattahpour SF, Hosseini P, Fard AM et al. AI and Deep Learning in Understanding the Etiology and Pathogenesis of Autoimmune Diseases. Kindle.4(1):1–182.

212. Chen YM Hsiao TH Lin CH Fann YC Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence J Biomed Sci 2025 32 1 16 10.1186/s12929-024-01110-w 39915780 PMC11804102 Chen YM, Hsiao TH, Lin CH, Fann YC. Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence. J Biomed Sci. 2025;32(1):16. 39915780 10.1186/s12929-024-01110-w PMC11804102

213. Basha SA, Hanirex DK, editors. Artificial Intelligence in Immunology: Predictive Models for Immune System Disorders. 2024 Global Conference on Communications and Information Technologies (GCCIT: IEEE.

214. Fritzler MJ, Mahler M. Precision medicine as an approach to autoimmune diseases. Precision Medicine and Artificial Intelligence: Academic. pp. 39–63.

215. Swetlik C Bove R McGinley M Clinical and research applications of the electronic medical record in multiple sclerosis: a narrative review of current uses and future applications Int J MS Care 2022 24 6 287 94 10.7224/1537-2073.2022-066 36545651 PMC9749832 Swetlik C, Bove R, McGinley M. Clinical and research applications of the electronic medical record in multiple sclerosis: a narrative review of current uses and future applications. Int J MS Care. 2022;24(6):287–94. 36545651 10.7224/1537-2073.2022-066 PMC9749832

216. Houssein EH Hosney ME Emam MM Younis EM Ali AA Mohamed WM Soft computing techniques for biomedical data analysis: open issues and challenges Artif Intell Rev 2023 56 Suppl 2 2599 649 10.1007/s10462-023-10585-2 Houssein EH, Hosney ME, Emam MM, Younis EM, Ali AA, Mohamed WM. Soft computing techniques for biomedical data analysis: open issues and challenges. Artif Intell Rev. 2023;56(Suppl 2):2599–649.

217. Gupta NS Kumar P Perspective of artificial intelligence in healthcare data management: a journey towards precision medicine Comput Biol Med 2023 162 107051 10.1016/j.compbiomed.2023.107051 37271113 Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: a journey towards precision medicine. Comput Biol Med. 2023;162:107051. 37271113 10.1016/j.compbiomed.2023.107051

218. Li TZ Still JM Xu K Lee HH Cai LY Krishnan AR Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification Med Image Comput Comput Assist Interv 2023 14221 649 59 38779102 10.1007/978-3-031-43895-0_61 PMC11110542 Li TZ, Still JM, Xu K, Lee HH, Cai LY, Krishnan AR, et al. Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification. Med Image Comput Comput Assist Interv. 2023;14221:649–59. 38779102 10.1007/978-3-031-43895-0_61 PMC11110542

219. Rane N, Choudhary S, Rane J. Explainable artificial intelligence (XAI) in healthcare: Interpretable models for clinical decision support.

220. Yarman BS, Rathore SPS. The Future of AI in Disease Detection—A Look at Emerging Trends and Future Directions in the Use of AI for Disease Detection and Diagnosis. AI in Disease Detection: Advancements and Applications. pp. 265–88.

221. Chaudhary G Unveiling the black box: bringing algorithmic transparency to AI Masaryk Univ J Law Technol 2024 18 1 93 122 10.5817/MUJLT2024-1-4 Chaudhary G. Unveiling the black box: bringing algorithmic transparency to AI. Masaryk Univ J Law Technol. 2024;18(1):93–122.

222. Natrayan L, Socrates S, Bharathi GB, Aluvala S, editors. A Framework for Automated Diagnosis and Management of Autoimmune Disorders with Neural Networks. 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC: IEEE.

223. ŞAhiN E Arslan NN Özdemir D Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning Neural Comput Appl 2025 37 2 859 965 10.1007/s00521-024-10437-2 ŞAhiN E, Arslan NN, Özdemir D. Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning. Neural Comput Appl. 2025;37(2):859–965.

224. Mo Q Bolideei M Rong S-J Luo J-H Yang C-L Lu W-Y GSK2334470 attenuates high salt-exacerbated rheumatoid arthritis progression by restoring Th17/Treg homeostasis iScience 2024 10.1016/j.isci.2024.109798 38947509 PMC11214488 Mo Q, Bolideei M, Rong S-J, Luo J-H, Yang C-L, Lu W-Y, et al. GSK2334470 attenuates high salt-exacerbated rheumatoid arthritis progression by restoring Th17/Treg homeostasis. iScience. 2024. 10.1016/j.isci.2024.109798. 38947509 10.1016/j.isci.2024.109798 PMC11214488

225. Sun Y Lin J Chen W Artificial intelligence in rheumatoid arthritis Rheumatology & Autoimmunity 2025 10.1002/rai2.12171 Sun Y, Lin J, Chen W. Artificial intelligence in rheumatoid arthritis. Rheumatology & Autoimmunity. 2025. 10.1002/rai2.12171.

226. Momtazmanesh S Nowroozi A Rezaei N Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review Rheumatol Ther 2022 9 5 1249 304 10.1007/s40744-022-00475-4 35849321 PMC9510088 Momtazmanesh S, Nowroozi A, Rezaei N. Artificial intelligence in rheumatoid arthritis: current status and future perspectives: a state-of-the-art review. Rheumatol Ther. 2022;9(5):1249–304. 35849321 10.1007/s40744-022-00475-4 PMC9510088

227. Sharma SD Bluett J Towards personalized medicine in rheumatoid arthritis Open Access Rheumatol Res Rev 2024 10.2147/OARRR.S372610 PMC11110814 38779469 Sharma SD, Bluett J. Towards personalized medicine in rheumatoid arthritis. Open Access Rheumatol Res Rev. 2024. 10.2147/OARRR.S372610. 10.2147/OARRR.S372610 PMC11110814 38779469

228. Alsaber AR Al-Herz A Alawadhi B Doush IA Setiya P Al-Sultan AT Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry Front Big Data 2024 7 1406365 10.3389/fdata.2024.1406365 39421133 PMC11484091 Alsaber AR, Al-Herz A, Alawadhi B, Doush IA, Setiya P, Al-Sultan AT, et al. Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry. Front Big Data. 2024;7:1406365. 39421133 10.3389/fdata.2024.1406365 PMC11484091

229. Koo BS, Eun S, Shin K, Yoon H, Hong C, Kim DH et al. Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics. Arthritis Res Therapy.23:1–10. 10.1186/s13075-021-02567-y PMC8259419 34229736

230. Benavent D Carmona L Llorente JFG Montoro M Ramirez S Otón T Empowering rheumatology through digital health technologies: contributions and barriers Exploration of Musculoskeletal Diseases 2024 2 2 92 105 10.37349/emd.2024.00038 Benavent D, Carmona L, Llorente JFG, Montoro M, Ramirez S, Otón T, et al. Empowering rheumatology through digital health technologies: contributions and barriers. Exploration of Musculoskeletal Diseases. 2024;2(2):92–105.

231. Capobianco E High-dimensional role of AI and machine learning in cancer research Br J Cancer 2022 126 4 523 32 10.1038/s41416-021-01689-z 35013580 PMC8854697 Capobianco E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer. 2022;126(4):523–32. 35013580 10.1038/s41416-021-01689-z PMC8854697

232. Klareskog L Rönnelid J Saevarsdottir S Padyukov L Alfredsson L The importance of differences: on environment and its interactions with genes and immunity in the causation of rheumatoid arthritis J Intern Med 2020 287 5 514 33 10.1111/joim.13058 32176395 Klareskog L, Rönnelid J, Saevarsdottir S, Padyukov L, Alfredsson L. The importance of differences: on environment and its interactions with genes and immunity in the causation of rheumatoid arthritis. J Intern Med. 2020;287(5):514–33. 32176395 10.1111/joim.13058

233. Zhang B Shi H Wang H Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach J Multidiscip Healthc 2023 10.2147/JMDH.S410301 37398894 PMC10312208 Zhang B, Shi H, Wang H. Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach. J Multidiscip Healthc. 2023. 10.2147/JMDH.S410301. 37398894 10.2147/JMDH.S410301 PMC10312208

234. Imtiaz M, Shah SAA, Rehman Z. A review of arthritis diagnosis techniques in the artificial intelligence era: Current trends and research challenges. Neurosci Inf.2(4):100079&#8211.

235. Afrazeh F, Shomalzadeh M. Revolutionizing Arthritis Care with Artificial Intelligence: A Comprehensive Review of Diagnostic, Prognostic, and Treatment Innovations. Int J Appl Data Sci Eng Health.1(2):7–17.

236. Mate GS Kureshi AK Singh BK An efficient CNN for hand X-ray classification of rheumatoid arthritis J Healthc Eng 2021 1 6712785 10.1155/2021/6712785 PMC8219419 34221300 Mate GS, Kureshi AK, Singh BK. An efficient CNN for hand X-ray classification of rheumatoid arthritis. J Healthc Eng. 2021;1:6712785. 10.1155/2021/6712785 PMC8219419 34221300

237. Bai L Zhang Y Wang P Zhu X Xiong JW Cui L Improved diagnosis of rheumatoid arthritis using an artificial neural network Sci Rep 2022 12 1 9810 10.1038/s41598-022-13750-9 35697754 PMC9192742 Bai L, Zhang Y, Wang P, Zhu X, Xiong JW, Cui L. Improved diagnosis of rheumatoid arthritis using an artificial neural network. Sci Rep. 2022;12(1):9810. 35697754 10.1038/s41598-022-13750-9 PMC9192742

238. Subhash MG, Kureshi AK. An efficient CNN for hand X-ray classification of rheumatoid arthritis. Microprocess Microsyst.104822.

239. Khan FN, Asim M, Qureshi MI. Artificial intelligence in the diagnosis and treatment of rheumatoid arthritis: Current status and prospects. Artificial Intelligence and Autoimmune Diseases: Applications in the Diagnosis, Prognosis, and Therapeutics. pp. 193–221.

240. Plant D Maciejewski M Smith S Nair N Rheumatoid Arthritis Consortium MTU Group RS Profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis Arthritis Rheumatol 2019 71 5 678 84 10.1002/art.40810 30615300 PMC9328381 Plant D, Maciejewski M, Smith S, Nair N, Rheumatoid Arthritis Consortium MTU, Group RS, et al. Profiling of gene expression biomarkers as a classifier of methotrexate nonresponse in patients with rheumatoid arthritis. Arthritis Rheumatol. 2019;71(5):678–84. 30615300 10.1002/art.40810 PMC9328381

241. Jian C Chen S Wang Z Zhou Y Zhang Y Li Z Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis BMC Med Inform Decis Mak 2023 23 1 148 10.1186/s12911-023-02248-7 37537590 PMC10398990 Jian C, Chen S, Wang Z, Zhou Y, Zhang Y, Li Z, et al. Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis. BMC Med Inform Decis Mak. 2023;23(1):148. 37537590 10.1186/s12911-023-02248-7 PMC10398990

242. Gosselt HR, Verhoeven MM, Bulatović-Ćalasan M, Welsing PM, Rotte MC, Hazes JM et al. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. J personalized Med.11(1):44&#8211. 10.3390/jpm11010044 PMC7828730 33466633

243. Parsaei A Moradi S Masoumi M Davatchi F Najafi A Kooshki AM Predictive value of erythrocyte sedimentation rate and C-reactive protein in Behcet’s disease activity and manifestations: a cross-sectional study BMC Rheumatol 2022 6 1 9 10.1186/s41927-021-00241-z 35144674 PMC8832718 Parsaei A, Moradi S, Masoumi M, Davatchi F, Najafi A, Kooshki AM, et al. Predictive value of erythrocyte sedimentation rate and C-reactive protein in Behcet’s disease activity and manifestations: a cross-sectional study. BMC Rheumatol. 2022;6(1):9. 35144674 10.1186/s41927-021-00241-z PMC8832718

244. Spiliopoulou A Colombo M Plant D Nair N Cui J Coenen MJ Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39 Ann Rheum Dis 2019 78 8 1055 61 10.1136/annrheumdis-2018-214877 31036624 PMC6669378 Spiliopoulou A, Colombo M, Plant D, Nair N, Cui J, Coenen MJ, et al. Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39. Ann Rheum Dis. 2019;78(8):1055–61. 31036624 10.1136/annrheumdis-2018-214877 PMC6669378

245. Mehran MJ Mohammadzadeh S Bolideei M Barzigar R Haider KH Jadgal N Artificial intelligence in drug discovery: integrative advances from data to therapeutic innovation Drug Dev Res 2026 87 2 e70229 10.1002/ddr.70229 41630488 Mehran MJ, Mohammadzadeh S, Bolideei M, Barzigar R, Haider KH, Jadgal N, et al. Artificial intelligence in drug discovery: integrative advances from data to therapeutic innovation. Drug Dev Res. 2026;87(2):e70229. 41630488 10.1002/ddr.70229

246. Kahlon MS Islam MM Vashishat A Raikwar S Revolutionizing rheumatoid arthritis care: AI-infused herbal treatments and the road ahead Curr Pharm Biotechnol 2025 26 3 316 8 10.2174/0113892010305528240506114408 38757330 Kahlon MS, Islam MM, Vashishat A, Raikwar S. Revolutionizing rheumatoid arthritis care: AI-infused herbal treatments and the road ahead. Curr Pharm Biotechnol. 2025;26(3):316–8. 38757330 10.2174/0113892010305528240506114408

247. Ivanisevic T Sewduth RN Multi-omics integration for the design of novel therapies and the identification of novel biomarkers Proteomes 2023 11 4 34 10.3390/proteomes11040034 37873876 PMC10594525 Ivanisevic T, Sewduth RN. Multi-omics integration for the design of novel therapies and the identification of novel biomarkers. Proteomes. 2023;11(4):34. 37873876 10.3390/proteomes11040034 PMC10594525

248. Ramchandani M Goyal AK Integrating omics data for personalized medicine in treating psoriasis Med Chem Res 2025 34 2 340 56 10.1007/s00044-024-03355-4 Ramchandani M, Goyal AK. Integrating omics data for personalized medicine in treating psoriasis. Med Chem Res. 2025;34(2):340–56.

249. Shi Y Zhou M Chang C Jiang P Wei K Zhao J Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management Front Immunol 2024 15 1409555 10.3389/fimmu.2024.1409555 38915408 PMC11194317 Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, et al. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol. 2024;15:1409555. 38915408 10.3389/fimmu.2024.1409555 PMC11194317

250. Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature. Seminars in Arthritis and Rheumatism. 61: WB Saunders. p. 152213&#8211. 10.1016/j.semarthrit.2023.152213 37315379

251. Du M Li S Jiang J Ma X Liu L Wang T Advances in the pathogenesis and treatment strategies for type 1 diabetes mellitus Int Immunopharmacol 2025 148 114185 10.1016/j.intimp.2025.114185 39893858 Du M, Li S, Jiang J, Ma X, Liu L, Wang T, et al. Advances in the pathogenesis and treatment strategies for type 1 diabetes mellitus. Int Immunopharmacol. 2025;148:114185. 39893858 10.1016/j.intimp.2025.114185

252. Azab ME, Amer H, Mohammwd W, ElSeddek M. Leveraging Artificial Intelligence in the Diagnosis and Management of Diabetic Foot Ulcers: A Review of Current Trends and Future Directions. Int J Telecommunications.5(01):1–26.

253. Schwarz PE Li J Lindstrom J Tuomilehto J Tools for predicting the risk of type 2 diabetes in daily practice Horm Metab Res 2009 41 02 86 97 10.1055/s-0028-1087203 19021089 Schwarz PE, Li J, Lindstrom J, Tuomilehto J. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res. 2009;41(02):86–97. 19021089 10.1055/s-0028-1087203

254. Ljubic B Hai AA Stanojevic M Diaz W Polimac D Pavlovski M Predicting complications of diabetes mellitus using advanced machine learning algorithms J Am Med Inform Assoc 2020 27 9 1343 51 10.1093/jamia/ocaa120 32869093 PMC7647294 Ljubic B, Hai AA, Stanojevic M, Diaz W, Polimac D, Pavlovski M, et al. Predicting complications of diabetes mellitus using advanced machine learning algorithms. J Am Med Inform Assoc. 2020;27(9):1343–51. 32869093 10.1093/jamia/ocaa120 PMC7647294

255. Dagliati A Marini S Sacchi L Cogni G Teliti M Tibollo V Machine learning methods to predict diabetes complications J Diabetes Sci Technol 2018 12 2 295 302 10.1177/1932296817706375 28494618 PMC5851210 Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol. 2018;12(2):295–302. 28494618 10.1177/1932296817706375 PMC5851210

256. Behera A. Use of artificial intelligence for management and identification of complications in diabetes. Clin Diabetol.10(2):221–5.

257. Kosaki K Tarumi T Sugawara J Tanahashi K Kumagai H Matsui M Renal hemodynamics across the adult lifespan: relevance of flow pulsatility to chronic kidney disease Exp Gerontol 2021 152 111459 10.1016/j.exger.2021.111459 34171394 Kosaki K, Tarumi T, Sugawara J, Tanahashi K, Kumagai H, Matsui M, et al. Renal hemodynamics across the adult lifespan: relevance of flow pulsatility to chronic kidney disease. Exp Gerontol. 2021;152:111459. 34171394 10.1016/j.exger.2021.111459

258. Khanam JJ Foo SY A comparison of machine learning algorithms for diabetes prediction ICT Express 2021 7 4 432 9 10.1016/j.icte.2021.02.004 Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432–9.

259. Rajendra P Latifi S Prediction of diabetes using logistic regression and ensemble techniques Comput Methods Programs Biomed Update 2021 1 100032 10.1016/j.cmpbup.2021.100032 Rajendra P, Latifi S. Prediction of diabetes using logistic regression and ensemble techniques. Comput Methods Programs Biomed Update. 2021;1:100032.

260. Ganie SM Pramanik PKD Bashir Malik M Mallik S Qin H An ensemble learning approach for diabetes prediction using boosting techniques Front Genet 2023 14 1252159 10.3389/fgene.2023.1252159 37953921 PMC10639159 Ganie SM, Pramanik PKD, Bashir Malik M, Mallik S, Qin H. An ensemble learning approach for diabetes prediction using boosting techniques. Front Genet. 2023;14:1252159. 37953921 10.3389/fgene.2023.1252159 PMC10639159

261. Cescon M DeSalvo DJ Ly TT Maahs DM Messer LH Buckingham BA Early detection of infusion set failure during insulin pump therapy in type 1 diabetes J Diabetes Sci Technol 2016 10 6 1268 76 10.1177/1932296816663962 27621142 PMC5094340 Cescon M, DeSalvo DJ, Ly TT, Maahs DM, Messer LH, Buckingham BA, et al. Early detection of infusion set failure during insulin pump therapy in type 1 diabetes. J Diabetes Sci Technol. 2016;10(6):1268–76. 27621142 10.1177/1932296816663962 PMC5094340

262. Russell WE Bundy BN Anderson MS Cooney LA Gitelman SE Goland RS Abatacept for Delay of Type 1 Diabetes Progression in Stage 1 Relatives at Risk: A Randomized, Double-Masked, Controlled Trial Diabetes Care 2023 46 5 1005 13 10.2337/dc22-2200 36920087 PMC10154649 Russell WE, Bundy BN, Anderson MS, Cooney LA, Gitelman SE, Goland RS, et al. Abatacept for Delay of Type 1 Diabetes Progression in Stage 1 Relatives at Risk: A Randomized, Double-Masked, Controlled Trial. Diabetes Care. 2023;46(5):1005–13. 36920087 10.2337/dc22-2200 PMC10154649

263. Orban T Bundy B Becker DJ DiMeglio LA Gitelman SE Goland R Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial Lancet 2011 378 9789 412 9 10.1016/S0140-6736(11)60886-6 21719096 PMC3462593 Orban T, Bundy B, Becker DJ, DiMeglio LA, Gitelman SE, Goland R, et al. Co-stimulation modulation with abatacept in patients with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled trial. Lancet. 2011;378(9789):412–9. 21719096 10.1016/S0140-6736(11)60886-6 PMC3462593

264. Seelig E, Howlett J, Porter L, Truman L, Heywood J, Kennet J et al. The DILfrequency study is an adaptive trial designed to identify the optimal IL-2 dosing regimen in patients with type 1 diabetes. JCI insight.3(19):99306&#8211. 10.1172/jci.insight.99306 PMC6237447 30282826

265. Waibel M Wentworth JM So M Couper JJ Cameron FJ MacIsaac RJ Baricitinib and β-cell function in patients with new-onset type 1 diabetes N Engl J Med 2023 389 23 2140 50 10.1056/NEJMoa2306691 38055252 Waibel M, Wentworth JM, So M, Couper JJ, Cameron FJ, MacIsaac RJ, et al. Baricitinib and β-cell function in patients with new-onset type 1 diabetes. N Engl J Med. 2023;389(23):2140–50. 38055252 10.1056/NEJMoa2306691

266. Rosenzwajg M Salet R Lorenzon R Tchitchek N Roux A Bernard C Low-dose IL-2 in children with recently diagnosed type 1 diabetes: a phase I/II randomised, double-blind, placebo-controlled, dose-finding study Diabetologia 2020 63 1808 21 10.1007/s00125-020-05200-w 32607749 Rosenzwajg M, Salet R, Lorenzon R, Tchitchek N, Roux A, Bernard C, et al. Low-dose IL-2 in children with recently diagnosed type 1 diabetes: a phase I/II randomised, double-blind, placebo-controlled, dose-finding study. Diabetologia. 2020;63:1808–21. 32607749 10.1007/s00125-020-05200-w

267. Tanoli Z Vähä-Koskela M Aittokallio T Artificial intelligence, machine learning, and drug repurposing in cancer Expert Opin Drug Discov 2021 16 9 977 89 10.1080/17460441.2021.1883585 33543671 Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov. 2021;16(9):977–89. 33543671 10.1080/17460441.2021.1883585

268. Hannelius U Beam CA Ludvigsson J Efficacy of GAD-alum immunotherapy associated with HLA-DR3-DQ2 in recently diagnosed type 1 diabetes Diabetologia 2020 63 2177 81 10.1007/s00125-020-05227-z 32754804 PMC7476912 Hannelius U, Beam CA, Ludvigsson J. Efficacy of GAD-alum immunotherapy associated with HLA-DR3-DQ2 in recently diagnosed type 1 diabetes. Diabetologia. 2020;63:2177–81. 32754804 10.1007/s00125-020-05227-z PMC7476912

269. Jacobsen LM Newby BN Perry DJ Posgai AL Haller MJ Brusko TM Immune mechanisms and pathways targeted in type 1 diabetes Curr Diabetes Rep 2018 18 1 20 10.1007/s11892-018-1066-5 PMC8053389 30168021 Jacobsen LM, Newby BN, Perry DJ, Posgai AL, Haller MJ, Brusko TM. Immune mechanisms and pathways targeted in type 1 diabetes. Curr Diabetes Rep. 2018;18:1–20. 10.1007/s11892-018-1066-5 PMC8053389 30168021

270. Nowak C, Lind M, Sumnik Z, Pelikanova T, Nattero-Chavez L, Lundberg E et al. Intralymphatic GAD-alum (Diamyd ® ) improves glycemic control in type 1 diabetes with HLA DR3-DQ2. J Clin Endocrinol Metabolism.107(9):2644–51. 10.1210/clinem/dgac343 PMC9721339 35665810

271. Freda PJ, Ghosh A, Zhang E, Luo T, Chitre AS, Polesskaya O et al. Automated quantitative trait locus analysis (AutoQTL. BioData Min.16(1):14&#8211. 10.1186/s13040-023-00331-3 PMC10088184 37038201

272. Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T- and B-cell epitope prediction. J Immunol Res.2017(1):2680160&#8211. 10.1155/2017/2680160 PMC5763123 29445754

273. Kacen A Javitt A Kramer MP Morgenstern D Tsaban T Shmueli MD Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors Nat Biotechnol 2023 41 2 239 51 10.1038/s41587-022-01464-2 36203013 PMC11197725 Kacen A, Javitt A, Kramer MP, Morgenstern D, Tsaban T, Shmueli MD, et al. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat Biotechnol. 2023;41(2):239–51. 36203013 10.1038/s41587-022-01464-2 PMC11197725

274. Katayama Y Yokota R Akiyama T Kobayashi TJ Machine learning approaches to TCR repertoire analysis Front Immunol 2022 13 858057 10.3389/fimmu.2022.858057 35911778 PMC9334875 Katayama Y, Yokota R, Akiyama T, Kobayashi TJ. Machine learning approaches to TCR repertoire analysis. Front Immunol. 2022;13:858057. 35911778 10.3389/fimmu.2022.858057 PMC9334875

275. Herrath M, Bain SC, Bode B, Clausen JO, Coppieters K, Gaysina L et al. Anti-interleukin-21 antibody and liraglutide for the preservation of β-cell function in adults with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled, phase 2 trial. Lancet Diabetes Endocrinol.9(4):212–24. 10.1016/S2213-8587(21)00019-X 33662334

276. Chou WC Lin Z Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling Toxicol Sci 2023 191 1 1 14 10.1093/toxsci/kfac101 36156156 PMC9887681 Chou WC, Lin Z. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci. 2023;191(1):1–14. 36156156 10.1093/toxsci/kfac101 PMC9887681

277. Long SA Rieck M Sanda S Bollyky JB Samuels PL Goland R Rapamycin/IL-2 combination therapy in patients with type 1 diabetes augments Tregs yet transiently impairs β-cell function Diabetes 2012 61 9 2340 8 10.2337/db12-0049 22721971 PMC3425404 Long SA, Rieck M, Sanda S, Bollyky JB, Samuels PL, Goland R, et al. Rapamycin/IL-2 combination therapy in patients with type 1 diabetes augments Tregs yet transiently impairs β-cell function. Diabetes. 2012;61(9):2340–8. 22721971 10.2337/db12-0049 PMC3425404

278. Karakose E, Wang X, Wang P, Carcamo S, Demircioglu D, Lambertini L et al. Single Cell RNA-Seq Analysis of Regenerative Drug-Treated Human Pancreatic Islets Identifies A Cycling Alpha Cell Population As Key Beta Cell Progenitors. 10.1016/j.xcrm.2024.101832 PMC11722108 39626675

279. Furukawa A, Wisel SA, Tang Q. Impact of immune-modulatory drugs on regulatory T cells. Transplantation.100(11):2288–300. 10.1097/TP.0000000000001379 PMC5077666 27490409

280. Tsonkova VG Sand FW Wolf XA Grunnet LG Ringgaard AK Ingvorsen C The EndoC-βH1 cell line is a valid model of human beta cells and applicable for screenings to identify novel drug target candidates Mol Metab 2018 8 144 57 10.1016/j.molmet.2017.12.007 29307512 PMC5985049 Tsonkova VG, Sand FW, Wolf XA, Grunnet LG, Ringgaard AK, Ingvorsen C, et al. The EndoC-βH1 cell line is a valid model of human beta cells and applicable for screenings to identify novel drug target candidates. Mol Metab. 2018;8:144–57. 29307512 10.1016/j.molmet.2017.12.007 PMC5985049

281. Deligne C You S Mallone R Personalized immunotherapies for type 1 diabetes: who, what, when, and how?> J Pers Med 2022 12 4 542 10.3390/jpm12040542 35455658 PMC9031881 Deligne C, You S, Mallone R. Personalized immunotherapies for type 1 diabetes: who, what, when, and how? J Pers Med. 2022;12(4):542. 35455658 10.3390/jpm12040542 PMC9031881

282. Pescovitz MD Greenbaum CJ Krause-Steinrauf H Becker DJ Gitelman SE Goland R Rituximab, B-lymphocyte depletion, and preservation of beta-cell function N Engl J Med 2009 361 22 2143 52 10.1056/NEJMoa0904452 19940299 PMC6410357 Pescovitz MD, Greenbaum CJ, Krause-Steinrauf H, Becker DJ, Gitelman SE, Goland R, et al. Rituximab, B-lymphocyte depletion, and preservation of beta-cell function. N Engl J Med. 2009;361(22):2143–52. 19940299 10.1056/NEJMoa0904452 PMC6410357

283. Noble JA Immunogenetics of type 1 diabetes: a comprehensive review J Autoimmun 2015 64 101 12 10.1016/j.jaut.2015.07.014 26272854 Noble JA. Immunogenetics of type 1 diabetes: a comprehensive review. J Autoimmun. 2015;64:101–12. 26272854 10.1016/j.jaut.2015.07.014

284. Shapiro MR, Tallon EM, Brown ME, Posgai AL, Clements MA, Brusko TM. Leveraging artificial intelligence and machine learning to accelerate the discovery of disease-modifying therapies in type 1 diabetes. Diabetologia.1–18. 10.1007/s00125-024-06339-6 PMC11832708 39694914

285. Cappon G Facchinetti A Digital twins in type 1 diabetes: a systematic review J Diabetes Sci Technol 2024 10.1177/19322968241262112 38887022 PMC11572256 Cappon G, Facchinetti A. Digital twins in type 1 diabetes: a systematic review. J Diabetes Sci Technol. 2024. 10.1177/19322968241262112. 38887022 10.1177/19322968241262112 PMC11572256

286. Rajpurkar P Chen E Banerjee O Topol EJ AI in health and medicine Nat Med 2022 28 1 31 8 10.1038/s41591-021-01614-0 35058619 Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8. 35058619 10.1038/s41591-021-01614-0

287. Karim MR, Islam T, Shajalal M, Beyan O, Lange C, Cochez M, et al. Explainable AI for bioinformatics: methods, tools, and applications. Brief Bioinform. 2023;24(5):236. 10.1093/bib/bbad236 37478371

288. Kırboğa KK Abbasi S Küçüksille EU Explainability and white box in drug discovery Chem Biol Drug Des 2023 102 1 217 33 10.1111/cbdd.14262 37105727 Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des. 2023;102(1):217–33. 37105727 10.1111/cbdd.14262

289. Vasquez VM Jr., McCabe M, McKee JC, Siby S, Hussain U, Faizuddin F, et al. Transforming cancer care: a narrative review on leveraging artificial intelligence to advance immunotherapy in underserved communities. J Clin Med. 2025. 10.3390/jcm14155346. 10.3390/jcm14155346 PMC12347673 40806968

290. Arora A Alderman JE Palmer J Ganapathi S Laws E McCradden MD The value of standards for health datasets in artificial intelligence-based applications Nat Med 2023 29 11 2929 38 10.1038/s41591-023-02608-w 37884627 PMC10667100 Arora A, Alderman JE, Palmer J, Ganapathi S, Laws E, McCradden MD, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29(11):2929–38. 37884627 10.1038/s41591-023-02608-w PMC10667100

291. Chen RJ Wang JJ Williamson DFK Chen TY Lipkova J Lu MY Algorithmic fairness in artificial intelligence for medicine and healthcare Nat Biomed Eng 2023 7 6 719 42 10.1038/s41551-023-01056-8 37380750 PMC10632090 Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7(6):719–42. 37380750 10.1038/s41551-023-01056-8 PMC10632090

292. Fahim YA Hasani IW Kabba S Ragab WM Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives Eur J Med Res 2025 30 1 848 10.1186/s40001-025-03196-w 40988064 PMC12455834 Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. Eur J Med Res. 2025;30(1):848. 40988064 10.1186/s40001-025-03196-w PMC12455834

293. Albani FG Alghamdi SS Almutairi MM Alqahtani T Artificial intelligence-driven innovations in oncology drug discovery: transforming traditional pipelines and enhancing drug design Drug Des Devel Ther 2025 19 5685 707 10.2147/DDDT.S509769 40626099 PMC12232943 Albani FG, Alghamdi SS, Almutairi MM, Alqahtani T. Artificial intelligence-driven innovations in oncology drug discovery: transforming traditional pipelines and enhancing drug design. Drug Des Devel Ther. 2025;19:5685–707. 40626099 10.2147/DDDT.S509769 PMC12232943

294. Maiorano MFP Cormio G Loizzi V Maiorano BA Artificial intelligence in ovarian cancer: a systematic review and meta-analysis of predictive AI models in genomics, radiomics, and immunotherapy AI 2025 6 4 84 10.3390/ai6040084 Maiorano MFP, Cormio G, Loizzi V, Maiorano BA. Artificial intelligence in ovarian cancer: a systematic review and meta-analysis of predictive AI models in genomics, radiomics, and immunotherapy. AI. 2025;6(4):84.

295. Fraser AG Biasin E Bijnens B Bruining N Caiani EG Cobbaert K Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives Expert Rev Med Devices 2023 20 6 467 91 10.1080/17434440.2023.2184685 37157833 Fraser AG, Biasin E, Bijnens B, Bruining N, Caiani EG, Cobbaert K, et al. Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives. Expert Rev Med Devices. 2023;20(6):467–91. 37157833 10.1080/17434440.2023.2184685

296. Kelly CJ Karthikesalingam A Suleyman M Corrado G King D Key challenges for delivering clinical impact with artificial intelligence BMC Med 2019 17 1 195 10.1186/s12916-019-1426-2 31665002 PMC6821018 Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. 31665002 10.1186/s12916-019-1426-2 PMC6821018

297. Abbas SR Abbas Z Zahir A Lee SW Federated learning in smart healthcare: a comprehensive review on privacy, security, and predictive analytics with IoT integration Healthcare 2024 12 24 2587 10.3390/healthcare12242587 39766014 PMC11728217 Abbas SR, Abbas Z, Zahir A, Lee SW. Federated learning in smart healthcare: a comprehensive review on privacy, security, and predictive analytics with IoT integration. Healthcare. 2024;12(24):2587. 39766014 10.3390/healthcare12242587 PMC11728217

298. Amezquita RA Lun ATL Becht E Carey VJ Carpp LN Geistlinger L Orchestrating single-cell analysis with Bioconductor Nat Methods 2020 17 2 137 45 10.1038/s41592-019-0654-x 31792435 PMC7358058 Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, et al. Orchestrating single-cell analysis with Bioconductor. Nat Methods. 2020;17(2):137–45. 31792435 10.1038/s41592-019-0654-x PMC7358058

299. Rieke N Hancox J Li W Milletarì F Roth HR Albarqouni S The future of digital health with federated learning NPJ Digit Med 2020 3 1 119 10.1038/s41746-020-00323-1 33015372 PMC7490367 Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119. 33015372 10.1038/s41746-020-00323-1 PMC7490367

300. Shen S Qi W Liu X Zeng J Li S Zhu X From virtual to reality: innovative practices of digital twins in tumor therapy J Transl Med 2025 23 1 348 10.1186/s12967-025-06371-z 40108714 PMC11921680 Shen S, Qi W, Liu X, Zeng J, Li S, Zhu X, et al. From virtual to reality: innovative practices of digital twins in tumor therapy. J Transl Med. 2025;23(1):348. 40108714 10.1186/s12967-025-06371-z PMC11921680

301. Chaudhuri A, Pash G, Hormuth DA, Lorenzo G, Kapteyn M, Wu C et al. Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Front Artif Intell. 2023;Volume 6–2023. 10.3389/frai.2023.1222612 PMC10598726 37886348

302. Samei E The future of in silico trials and digital twins in medicine PNAS Nexus 2025 4 5 pgaf123 10.1093/pnasnexus/pgaf123 40313535 PMC12043051 Samei E. The future of in silico trials and digital twins in medicine. PNAS Nexus. 2025;4(5):pgaf123. 40313535 10.1093/pnasnexus/pgaf123 PMC12043051

303. Liu X Cruz Rivera S Moher D Calvert MJ Denniston AK Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension Nat Med 2020 26 9 1364 74 10.1038/s41591-020-1034-x 32908283 PMC7598943 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364–74. 32908283 10.1038/s41591-020-1034-x PMC7598943

304. Zhang C-c Feng H-r Zhu J Hong W-f Application of spatial and single-cell omics in tumor immunotherapy biomarkers Labmed Discov 2025 10.1016/j.lmd.2025.100076 Zhang C-c, Feng H-r, Zhu J, Hong W-f. Application of spatial and single-cell omics in tumor immunotherapy biomarkers. Labmed Discov. 2025. 10.1016/j.lmd.2025.100076.

305. Li Y Wu X Fang D Luo Y Informing immunotherapy with multi-omics driven machine learning NPJ Digit Med 2024 7 1 67 10.1038/s41746-024-01043-6 38486092 PMC10940614 Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med. 2024;7(1):67. 38486092 10.1038/s41746-024-01043-6 PMC10940614

306. Wei F Azuma K Nakahara Y Saito H Matsuo N Tagami T Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures J Immunother Cancer 2023 10.1136/jitc-2023-006788 37433717 PMC10347453 Wei F, Azuma K, Nakahara Y, Saito H, Matsuo N, Tagami T, et al. Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures. J Immunother Cancer. 2023. 10.1136/jitc-2023-006788. 37433717 10.1136/jitc-2023-006788 PMC10347453

307. Zhao Z Li Y Wu Y Chen R Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma Cancer Biomark 2020 27 1 19 28 10.3233/CBM-190380 31658045 PMC12662277 Zhao Z, Li Y, Wu Y, Chen R. Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma. Cancer Biomark. 2020;27(1):19–28. 31658045 10.3233/CBM-190380 PMC12662277

308. He Q Xiao B Tan Y Wang J Tan H Peng C Integrated multicenter deep learning system for prognostic prediction in bladder cancer npj Precision Oncol 2024 8 1 233 10.1038/s41698-024-00731-6 PMC11484793 39414931 He Q, Xiao B, Tan Y, Wang J, Tan H, Peng C, et al. Integrated multicenter deep learning system for prognostic prediction in bladder cancer. npj Precision Oncol. 2024;8(1):233. 10.1038/s41698-024-00731-6 PMC11484793 39414931

309. Nie W Jiang Y Yao L Zhu X Al-Danakh AY Liu W Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models Heliyon 2024 10 23 e39327 10.1016/j.heliyon.2024.e39327 39687145 PMC11647853 Nie W, Jiang Y, Yao L, Zhu X, Al-Danakh AY, Liu W, et al. Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models. Heliyon. 2024;10(23):e39327. 39687145 10.1016/j.heliyon.2024.e39327 PMC11647853

310. Sammut S-J Crispin-Ortuzar M Chin S-F Provenzano E Bardwell HA Ma W Multi-omic machine learning predictor of breast cancer therapy response Nature 2022 601 7894 623 9 10.1038/s41586-021-04278-5 34875674 PMC8791834 Sammut S-J, Crispin-Ortuzar M, Chin S-F, Provenzano E, Bardwell HA, Ma W, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022;601(7894):623–9. 34875674 10.1038/s41586-021-04278-5 PMC8791834

311. Wang HN An JH Wang FQ Hu WQ Zong L Predicting gastric cancer survival using machine learning: a systematic review World J Gastrointest Oncol 2025 17 5 103804 10.4251/wjgo.v17.i5.103804 40487963 PMC12142261 Wang HN, An JH, Wang FQ, Hu WQ, Zong L. Predicting gastric cancer survival using machine learning: a systematic review. World J Gastrointest Oncol. 2025;17(5):103804. 40487963 10.4251/wjgo.v17.i5.103804 PMC12142261

312. Zhou C-M Wang Y Yang J-J Zhu Y Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology BMC Med Inform Decis Mak 2023 23 1 53 10.1186/s12911-023-02150-2 37004065 PMC10067164 Zhou C-M, Wang Y, Yang J-J, Zhu Y. Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology. BMC Med Inform Decis Mak. 2023;23(1):53. 37004065 10.1186/s12911-023-02150-2 PMC10067164

313. Liu L Zhang Y Zhao X-h Zhu M Liang J-w Jiang Z-y Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images Comput Methods Programs Biomed 2025 269 108895 10.1016/j.cmpb.2025.108895 40513510 Liu L, Zhang Y, Zhao X-h, Zhu M, Liang J-w, Jiang Z-y, et al. Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images. Comput Methods Programs Biomed. 2025;269:108895. 40513510 10.1016/j.cmpb.2025.108895

314. Liu X Tao P Su H Li Y Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer Sci Rep 2025 15 1 4170 10.1038/s41598-025-88812-9 39905263 PMC11794614 Liu X, Tao P, Su H, Li Y. Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer. Sci Rep. 2025;15(1):4170. 39905263 10.1038/s41598-025-88812-9 PMC11794614

315. Li HJ Qiu ZB Wang MM Zhang C Hong HZ Fu R Radiomics-based support vector machine distinguishes molecular events driving the progression of lung adenocarcinoma J Thorac Oncol 2025 20 1 52 64 10.1016/j.jtho.2024.09.1431 39306192 Li HJ, Qiu ZB, Wang MM, Zhang C, Hong HZ, Fu R, et al. Radiomics-based support vector machine distinguishes molecular events driving the progression of lung adenocarcinoma. J Thorac Oncol. 2025;20(1):52–64. 39306192 10.1016/j.jtho.2024.09.1431

316. Mazzaschi G Milanese G Pagano P Madeddu D Gnetti L Trentini F Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC Lung Cancer 2020 144 30 9 10.1016/j.lungcan.2020.04.006 32361033 Mazzaschi G, Milanese G, Pagano P, Madeddu D, Gnetti L, Trentini F, et al. Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC. Lung Cancer. 2020;144:30–9. 32361033 10.1016/j.lungcan.2020.04.006

317. Tong H, Sun J, Fang J, Zhang M, Liu H, Xia R et al. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study. Front Immunol. 2022;Volume 13–2022. 10.3389/fimmu.2022.859323 PMC9105942 35572597

318. Li C Zhou Z Hou L Hu K Wu Z Xie Y A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics Comput Biol Med 2024 178 108638 10.1016/j.compbiomed.2024.108638 38897152 Li C, Zhou Z, Hou L, Hu K, Wu Z, Xie Y, et al. A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics. Comput Biol Med. 2024;178:108638. 38897152 10.1016/j.compbiomed.2024.108638

319. Chuang C-C Liu Y-C Ou Y-Y DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks Int J Biol Macromol 2024 281 136252 10.1016/j.ijbiomac.2024.136252 39366619 Chuang C-C, Liu Y-C, Ou Y-Y. DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks. Int J Biol Macromol. 2024;281:136252. 39366619 10.1016/j.ijbiomac.2024.136252

320. Tripathi S Moyer EJ Augustin AI Zavalny A Dheer S Sukumaran R RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer Inf Med Unlock 2022 33 101062 10.1016/j.imu.2022.101062 Tripathi S, Moyer EJ, Augustin AI, Zavalny A, Dheer S, Sukumaran R, et al. RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer. Inf Med Unlock. 2022;33:101062.

321. Machaca V Goyzueta V Cruz MG Sejje E Pilco LM López J Transformers meets neoantigen detection: a systematic literature review J Integr Bioinform 2024 10.1515/jib-2023-0043 38960869 PMC11377031 Machaca V, Goyzueta V, Cruz MG, Sejje E, Pilco LM, López J, et al. Transformers meets neoantigen detection: a systematic literature review. J Integr Bioinform. 2024. 10.1515/jib-2023-0043. 38960869 10.1515/jib-2023-0043 PMC11377031

322. Menyhárt O Győrffy B Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis Comput Struct Biotechnol J 2021 19 949 60 10.1016/j.csbj.2021.01.009 33613862 PMC7868685 Menyhárt O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J. 2021;19:949–60. 33613862 10.1016/j.csbj.2021.01.009 PMC7868685

323. Ju H Kim K Kim BI Woo S-K Graph neural network model for prediction of non-small cell lung cancer lymph node metastasis using protein–protein interaction network and 18F-FDG PET/CT radiomics Int J Mol Sci 2024 25 2 698 10.3390/ijms25020698 38255770 PMC10815846 Ju H, Kim K, Kim BI, Woo S-K. Graph neural network model for prediction of non-small cell lung cancer lymph node metastasis using protein–protein interaction network and 18F-FDG PET/CT radiomics. Int J Mol Sci. 2024;25(2):698. 38255770 10.3390/ijms25020698 PMC10815846

324. Khan A Sajid MZ Khan NA Youssef A Abbas Q CAD-Skin: a hybrid convolutional neural network–autoencoder framework for precise detection and classification of skin lesions and cancer Bioengineering 2025 12 4 326 10.3390/bioengineering12040326 40281686 PMC12025204 Khan A, Sajid MZ, Khan NA, Youssef A, Abbas Q. CAD-Skin: a hybrid convolutional neural network–autoencoder framework for precise detection and classification of skin lesions and cancer. Bioengineering. 2025;12(4):326. 40281686 10.3390/bioengineering12040326 PMC12025204

325. Fang X Chong CF Wong KL Simões M Ng BK Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification Sci Rep 2025 15 1 2146 10.1038/s41598-024-84836-9 39820368 PMC11739633 Fang X, Chong CF, Wong KL, Simões M, Ng BK. Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification. Sci Rep. 2025;15(1):2146. 39820368 10.1038/s41598-024-84836-9 PMC11739633

326. Yang R Li W Yu S Wu Z Zhang H Liu X Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: a novel diagnostic tool Int J Med Inform 2025 193 105694 10.1016/j.ijmedinf.2024.105694 39515045 Yang R, Li W, Yu S, Wu Z, Zhang H, Liu X, et al. Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: a novel diagnostic tool. Int J Med Inform. 2025;193:105694. 39515045 10.1016/j.ijmedinf.2024.105694

327. Wagner J Oldenburg J Nath N Simm S Explainable AI model reveals informative mutational signatures for cancer-type classification Cancers 2025 17 11 1731 10.3390/cancers17111731 40507213 PMC12153866 Wagner J, Oldenburg J, Nath N, Simm S. Explainable AI model reveals informative mutational signatures for cancer-type classification. Cancers. 2025;17(11):1731. 40507213 10.3390/cancers17111731 PMC12153866

328. Abas Mohamed Y Ee Khoo B Shahrimie Mohd Asaari M Ezane Aziz M Rahiman Ghazali F Decoding the black box: explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-a state-of-the art systematic review Int J Med Inform 2025 193 105689 10.1016/j.ijmedinf.2024.105689 39522406 Abas Mohamed Y, Ee Khoo B, Shahrimie Mohd Asaari M, Ezane Aziz M, Rahiman Ghazali F. Decoding the black box: explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-a state-of-the art systematic review. Int J Med Inform. 2025;193:105689. 39522406 10.1016/j.ijmedinf.2024.105689

329. Singhal A Agrawal KK Quezada A Aguiñaga AR Jiménez S Yadav SP Explainable artificial intelligence (XAI) model for cancer image classification CMES Comput Model Eng Sci 2024 141 1 401 41 Singhal A, Agrawal KK, Quezada A, Aguiñaga AR, Jiménez S, Yadav SP. Explainable artificial intelligence (XAI) model for cancer image classification. CMES Comput Model Eng Sci. 2024;141(1):401–41.

330. Maithresh A, Nikhil V, Saipuneeth H, Reddy GVS, editors. Exploring the Superiority of CapsNet over CNN For Early Detection of Lung Cancer A Comparative Analysis. 2023 International Conference on Inventive Computation Technologies (ICICT); 2023 26–28 April 2023.

331. Bushara AR Vinod Kumar RS Kumar SS An ensemble method for the detection and classification of lung cancer using computed tomography images utilizing a capsule network with Visual Geometry Group Biomed Signal Process Control 2023 85 104930 10.1016/j.bspc.2023.104930 Bushara AR, Vinod Kumar RS, Kumar SS. An ensemble method for the detection and classification of lung cancer using computed tomography images utilizing a capsule network with Visual Geometry Group. Biomed Signal Process Control. 2023;85:104930.

332. Ali M Ali R Multi-input dual-stream capsule network for improved lung and colon cancer classification Diagnostics 2021 10.3390/diagnostics11081485 34441419 PMC8393706 Ali M, Ali R. Multi-input dual-stream capsule network for improved lung and colon cancer classification. Diagnostics. 2021. 10.3390/diagnostics11081485. 34441419 10.3390/diagnostics11081485 PMC8393706

333. Ochoa-Ornelas R Gudiño-Ochoa A García-Rodríguez JA A hybrid deep learning and machine learning approach with Mobile-EfficientNet and Grey Wolf Optimizer for lung and colon cancer histopathology classification Cancers 2024 16 22 3791 10.3390/cancers16223791 39594746 PMC11593226 Ochoa-Ornelas R, Gudiño-Ochoa A, García-Rodríguez JA. A hybrid deep learning and machine learning approach with Mobile-EfficientNet and Grey Wolf Optimizer for lung and colon cancer histopathology classification. Cancers. 2024;16(22):3791. 39594746 10.3390/cancers16223791 PMC11593226

334. Gowthamy J Ramesh S A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM Expert Syst Appl 2024 252 124114 10.1016/j.eswa.2024.124114 Gowthamy J, Ramesh S. A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM. Expert Syst Appl. 2024;252:124114.

335. Debellotte O Dookie RL Rinkoo F Kar A Salazar González JF Saraf P Artificial intelligence and early detection of breast, lung, and colon cancer: a narrative review Cureus 2025 17 2 e79199 40125138 10.7759/cureus.79199 PMC11926462 Debellotte O, Dookie RL, Rinkoo F, Kar A, Salazar González JF, Saraf P, et al. Artificial intelligence and early detection of breast, lung, and colon cancer: a narrative review. Cureus. 2025;17(2):e79199. 40125138 10.7759/cureus.79199 PMC11926462

336. Krishnapriya S Birudaraju H Madhulatha M Nagajyothi S Ranadheer Kumar KS Hybrid deep learning model for identifying the cancer type BenchCouncil Trans Benchmarks Stand Eval 2025 5 2 100211 10.1016/j.tbench.2025.100211 Krishnapriya S, Birudaraju H, Madhulatha M, Nagajyothi S, Ranadheer Kumar KS. Hybrid deep learning model for identifying the cancer type. BenchCouncil Trans Benchmarks Stand Eval. 2025;5(2):100211.

337. Yang Y Wang J Xie F Liu J Shu C Wang Y A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists Comput Biol Med 2021 139 104924 10.1016/j.compbiomed.2021.104924 34688173 Yang Y, Wang J, Xie F, Liu J, Shu C, Wang Y, et al. A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists. Comput Biol Med. 2021;139:104924. 34688173 10.1016/j.compbiomed.2021.104924

338. Alhamdani MA, Hashim AN, editors. Classification of psoriasis and eczema based on Convolutional Neural Network. 2022 Iraqi International Conference on Communication and Information Technologies (IICCIT); 2022 7–8 Sept. 2022.

339. Adebiyi MO, Destiny O, Olaniyan D, Olaniyan J, Oluwatomi D, editors. Systemic Lupus Erythematosus (SLE) Detection Using Deep Learning. 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG); 2024 2–4 April 2024.

340. Rajimehr R Farsiu S Kouhsari LM Bidari A Lucas C Yousefian S Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks Lupus 2002 11 8 485 92 10.1191/0961203302lu226oa 12220102 Rajimehr R, Farsiu S, Kouhsari LM, Bidari A, Lucas C, Yousefian S, et al. Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks. Lupus. 2002;11(8):485–92. 12220102 10.1191/0961203302lu226oa

341. Bai L Zhang Y Wang P Zhu X Xiong J-W Cui L Improved diagnosis of rheumatoid arthritis using an artificial neural network Sci Rep 2022 12 1 9810 10.1038/s41598-022-13750-9 35697754 PMC9192742 Bai L, Zhang Y, Wang P, Zhu X, Xiong J-W, Cui L. Improved diagnosis of rheumatoid arthritis using an artificial neural network. Sci Rep. 2022;12(1):9810. 35697754 10.1038/s41598-022-13750-9 PMC9192742

342. Sharifmousavi SS Borhani MS Support vectors machine-based model for diagnosis of multiple sclerosis using the plasma levels of selenium, vitamin B12, and vitamin D3 Inf Med Unlocked 2020 20 100382 10.1016/j.imu.2020.100382 Sharifmousavi SS, Borhani MS. Support vectors machine-based model for diagnosis of multiple sclerosis using the plasma levels of selenium, vitamin B12, and vitamin D3. Inf Med Unlocked. 2020;20:100382.

343. Alivernini S Cañete JD Bacardit J Kurowska-Stolarska M Using explainable artificial intelligence to predict and forestall flare in rheumatoid arthritis Nat Med 2024 30 4 925 6 10.1038/s41591-024-02818-w 38361121 Alivernini S, Cañete JD, Bacardit J, Kurowska-Stolarska M. Using explainable artificial intelligence to predict and forestall flare in rheumatoid arthritis. Nat Med. 2024;30(4):925–6. 38361121 10.1038/s41591-024-02818-w

344. Pontiveros MJ, Solano GA, Tee CA, Tee ML, editors. Explainable machine learning applied to single-nucleotide polymorphisms for systemic lupus erythematosus prediction. 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA; 2020: IEEE.

345. Lee C Joo G Shin S Im H Moon KW Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning Sci Rep 2023 13 1 21800 10.1038/s41598-023-48842-7 38066096 PMC10709305 Lee C, Joo G, Shin S, Im H, Moon KW. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Sci Rep. 2023;13(1):21800. 38066096 10.1038/s41598-023-48842-7 PMC10709305

346. Lee S Kang S Eun Y Won H-H Kim H Lee J Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis Arthritis Res therapy 2021 23 1 12 10.1186/s13075-021-02635-3 PMC8501710 34627335 Lee S, Kang S, Eun Y, Won H-H, Kim H, Lee J, et al. Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Res therapy. 2021;23:1–12. 10.1186/s13075-021-02635-3 PMC8501710 34627335

347. Aijaz SF Khan SJ Azim F Shakeel CS Hassan U Deep learning application for effective classification of different types of psoriasis J Healthc Eng 2022 2022 1 7541583 35075392 10.1155/2022/7541583 PMC8783723 Aijaz SF, Khan SJ, Azim F, Shakeel CS, Hassan U. Deep learning application for effective classification of different types of psoriasis. J Healthc Eng. 2022;2022(1):7541583. 35075392 10.1155/2022/7541583 PMC8783723

348. de Ponce Leon-Sanchez ER Dominguez-Ramirez OA Herrera-Navarro AM Rodriguez-Resendiz J Paredes-Orta C Mendiola-Santibañez JD A deep learning approach for predicting multiple sclerosis Micromachines 2023 14 4 749 10.3390/mi14040749 37420982 PMC10141207 de Ponce Leon-Sanchez ER, Dominguez-Ramirez OA, Herrera-Navarro AM, Rodriguez-Resendiz J, Paredes-Orta C, Mendiola-Santibañez JD. A deep learning approach for predicting multiple sclerosis. Micromachines. 2023;14(4):749. 37420982 10.3390/mi14040749 PMC10141207

349. Williams DD Ferro D Mullaney C Skrabonja L Barnes MS Patton SR An “all-data-on-hand” deep learning model to predict hospitalization for diabetic ketoacidosis in youth with type 1 diabetes: development and validation study JMIR Diabetes 2023 8 e47592 10.2196/47592 37224506 PMC10394604 Williams DD, Ferro D, Mullaney C, Skrabonja L, Barnes MS, Patton SR, et al. An “all-data-on-hand” deep learning model to predict hospitalization for diabetic ketoacidosis in youth with type 1 diabetes: development and validation study. JMIR Diabetes. 2023;8:e47592. 37224506 10.2196/47592 PMC10394604

350. Norgeot B Glicksberg BS Trupin L Lituiev D Gianfrancesco M Oskotsky B Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis JAMA Netw Open 2019 2 3 e190606 e 10.1001/jamanetworkopen.2019.0606 30874779 PMC6484652 Norgeot B, Glicksberg BS, Trupin L, Lituiev D, Gianfrancesco M, Oskotsky B, et al. Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Netw Open. 2019;2(3):e190606-e. 30874779 10.1001/jamanetworkopen.2019.0606 PMC6484652

351. Shehzadi N Rehman A Naz S Rehman S Khalifa F Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future Artif Intell Rev 2025 58 9 1 47 10.1007/s10462-025-11195-w Shehzadi N, Rehman A, Naz S, Rehman S, Khalifa F. Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future. Artif Intell Rev. 2025;58(9):1–47.

352. Pinto MF Oliveira H Batista S Cruz L Pinto M Correia I Prediction of disease progression and outcomes in multiple sclerosis with machine learning Sci Rep 2020 10 1 21038 10.1038/s41598-020-78212-6 33273676 PMC7713436 Pinto MF, Oliveira H, Batista S, Cruz L, Pinto M, Correia I, et al. Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Sci Rep. 2020;10(1):21038. 33273676 10.1038/s41598-020-78212-6 PMC7713436

353. Gu X-x Jin Y Fu T Zhang X-m Li T Yang Y Relevant characteristics analysis using natural language processing and machine learning based on phenotypes and T-cell subsets in Systemic Lupus Erythematosus patients with anxiety Front Psychiatry 2021 12 793505 10.3389/fpsyt.2021.793505 34955935 PMC8703039 Gu X-x, Jin Y, Fu T, Zhang X-m, Li T, Yang Y, et al. Relevant characteristics analysis using natural language processing and machine learning based on phenotypes and T-cell subsets in Systemic Lupus Erythematosus patients with anxiety. Front Psychiatry. 2021;12:793505. 34955935 10.3389/fpsyt.2021.793505 PMC8703039

354. Deng Y, Pacheco JA, Chung A, Mao C, Smith JC, Zhao J et al. Natural language processing to identify lupus nephritis phenotype in electronic health records. arXiv preprint arXiv:211210821. 2021. 10.1186/s12911-024-02420-7 PMC10910523 38433189

355. Omar M Naffaa ME Glicksberg BS Reuveni H Nadkarni GN Klang E Advancing rheumatology with natural language processing: insights and prospects from a systematic review Rheumatol Adv Pract 2024 8 4 rkae120 10.1093/rap/rkae120 39399162 PMC11467191 Omar M, Naffaa ME, Glicksberg BS, Reuveni H, Nadkarni GN, Klang E. Advancing rheumatology with natural language processing: insights and prospects from a systematic review. Rheumatol Adv Pract. 2024;8(4):rkae120. 39399162 10.1093/rap/rkae120 PMC11467191

356. Gharehali S Rahatabad FN Einalou Z Modeling multiple sclerosis at different levels using reinforcement learning Int Clin Neurosci J 2018 5 3 98 102 10.15171/icnj.2018.19 Gharehali S, Rahatabad FN, Einalou Z. Modeling multiple sclerosis at different levels using reinforcement learning. Int Clin Neurosci J. 2018;5(3):98–102.

357. Shiezadeh Z Sajedi H Aflakie E Diagnosis of rheumatoid arthritis using an ensemble learning approach Comput Sci Inf Technol(CS IT) 2015 5 15 139 48 Shiezadeh Z, Sajedi H, Aflakie E. Diagnosis of rheumatoid arthritis using an ensemble learning approach. Comput Sci Inf Technol(CS IT). 2015;5(15):139–48.

358. Wang L Zhu L Jiang J Wang L Ni W Decision tree analysis for evaluating disease activity in patients with rheumatoid arthritis J Int Med Res 2021 49 10 03000605211053232 10.1177/03000605211053232 34670422 PMC8543724 Wang L, Zhu L, Jiang J, Wang L, Ni W. Decision tree analysis for evaluating disease activity in patients with rheumatoid arthritis. J Int Med Res. 2021;49(10):03000605211053232. 34670422 10.1177/03000605211053232 PMC8543724

359. Katsarou DN Georga EI Christou MA Christou PA Tigas S Papaloukas C Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling BMC Med Inform Decis Mak 2025 25 1 46 10.1186/s12911-025-02867-2 39891137 PMC11783934 Katsarou DN, Georga EI, Christou MA, Christou PA, Tigas S, Papaloukas C, et al. Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling. BMC Med Inform Decis Mak. 2025;25(1):46. 39891137 10.1186/s12911-025-02867-2 PMC11783934

360. Fukae J Amasaki Y Fujieda Y Sone Y Katagishi K Horie T Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints J Int Med Res 2025 53 2 03000605251318195 10.1177/03000605251318195 39904596 PMC11795604 Fukae J, Amasaki Y, Fujieda Y, Sone Y, Katagishi K, Horie T, et al. Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints. J Int Med Res. 2025;53(2):03000605251318195. 39904596 10.1177/03000605251318195 PMC11795604

361. Voigt I Inojosa H Dillenseger A Haase R Akgün K Ziemssen T Digital twins for multiple sclerosis Front Immunol 2021 12 669811 10.3389/fimmu.2021.669811 34012452 PMC8128142 Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital twins for multiple sclerosis. Front Immunol. 2021;12:669811. 34012452 10.3389/fimmu.2021.669811 PMC8128142

362. Cen S Gebregziabher M Moazami S Azevedo CJ Pelletier D Toward precision medicine using a “digital twin” approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis Sci Rep 2023 13 1 16279 10.1038/s41598-023-43618-5 37770560 PMC10539386 Cen S, Gebregziabher M, Moazami S, Azevedo CJ, Pelletier D. Toward precision medicine using a “digital twin” approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis. Sci Rep. 2023;13(1):16279. 37770560 10.1038/s41598-023-43618-5 PMC10539386

363. Cappon G Vettoretti M Sparacino G Del Favero S Facchinetti A Replaybg: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative therapies IEEE Trans Biomed Eng 2023 70 11 3227 38 10.1109/TBME.2023.3286856 37368794 Cappon G, Vettoretti M, Sparacino G, Del Favero S, Facchinetti A. Replaybg: a digital twin-based methodology to identify a personalized model from type 1 diabetes data and simulate glucose concentrations to assess alternative therapies. IEEE Trans Biomed Eng. 2023;70(11):3227–38. 37368794 10.1109/TBME.2023.3286856