Результаты поиска

Laser Interstitial Thermal Therapy for High-Grade Gliomas: Current Evidence, Clinical Applications and Emerging Role of Artificial Intelligence.

Background: High-grade gliomas pose formidable challenges in neuro-oncology, with a median overall survival (OS) of 12-18 months. Laser interstitial thermal therapy (LITT) offers a minimally invasive cytoreductive option for deep-seated or recurrent tumors, achieving ablation rates of 85-98%. In parallel, artificial intelligence and machine learning are increasingly being applied to neuro-oncology to improve diagnosis, treatment planning, and outcome prediction, although these applications remain largely investigational. Methods: A literature search was conducted using the PubMed, MEDLINE, Emb…

Use of Artificial Intelligence to Predict Outcomes in Critically Ill Patients Receiving Extracorporeal Membrane Oxygenation: A Systematic Review.

Extracorporeal membrane oxygenation (ECMO) is widely used in patients with severe cardiac or respiratory failure. ECMO is resource and cost intensive and carries the potential for significant complications; therefore, identifying patients who would derive the most benefit and personalizing its application are important. A number of scoring systems have previously been developed to predict the outcomes of patients receiving ECMO. Machine learning (ML)-based models are highly effective at incorporating the large amount of complex data and large number of variables that may be associated with out

Deciphering pharmaceutical resistance in sulfur-driven autotrophic denitrification: an integrated multi-omics artificial intelligence-driven structural biology approach.

Sulfur-driven autotrophic denitrification (SdAD) is a promising low-carbon technology for nitrogen removal; however, its stability and adapt … ization and metabolic compensation. Methodologically, this work highlights the value of integrating artificial intelligence-driven structural biology with multi-omics analyses to decode the mechanisms of contaminant resistance in biological wastewater treatment systems.

Safety Monitoring of High-Risk Antibiotics Using Artificial Intelligence: A Narrative Review with Focus on Real-World Evidence.

High-risk antibiotics remain indispensable in contemporary infectious diseases practice, yet they account for a disproportionate share of preventable toxicity, therapeutic drug monitoring complexity, and antimicrobial stewardship workload. Vancomycin, aminoglycosides, colistin, linezolid, daptomycin, selected beta-lactams, and amphotericin B are particularly challenging because clinically relevant exposure-toxicity relationships coexist with marked inter-patient variability and fragmented post-marketing safety surveillance. Artificial intelligence and real-world evidence are increasingly propo…

Evaluation of an Artificial Intelligence Communication Platform for Racial Biases in Pretest Education About Prostate Cancer Germline Testing.

PURPOSE: Guidelines recommend germline testing in advanced prostate cancer (PCa) to inform treatment and personal/familial cancer risk, yet Black patients are less likely than White patients to complete testing. Artificial intelligence (AI) tools are increasingly used in pretest education to improve access but remain unevaluated for racial bias. MATERIALS AND METHODS: We developed ProGene, a secure, generative AI chatbot for PCa germline testing education in Black patients. We prompted ProGene with seven questions about testing types, personal benefits, family benefits, drawbacks, logistics, …

Integrating human and artificial intelligence for robust postmarketing safety surveillance systems: reflections from the FDA Sentinel Innovation Center.

OBJECTIVES: To describe considerations for integration of human and artificial intelligence for creating a postmarketing surveillance system capable of timely and reliably identifying causal effects of medications on safety endpoints. MATERIALS AND METHODS: The FDA has prioritized more extensive Electronic Health Records (EHR) integration along with generative artificial intelligence and machine learning (Gen AI/ML) into the national active surveillance program for medical products-the Sentinel Initiative. Based on our experience of leading these efforts, we provide perspectives on the opport…

Quantum-enhanced generative artificial intelligence: a critical review of classical limitations, complexity barriers, and hybrid quantum-classical architectures.

The rapid commercialization of generative artificial intelligence (AI), along with the maturation of quantum technologies has raised a question: can quantum-powered neural networks become the next major shift in large language model (LLM) technology? This naturally leads to another misconception that quantum systems will replace classical LLMs. In this study, both architectures are compared in a contrastive manner in terms of mathematics. The data reveals that identical dynamics that help classical systems learn natural language distributions constrain its ability to use efficient sampling of …

Artificial Intelligence for Integrated Analysis of Non-Blood Biological Fluids: From Biomarker Discovery to Clinical Decision-Support Systems.

The analysis of non-blood biological fluids, including cerebrospinal fluid (CSF), serous effusions, and synovial fluid, plays a central role in laboratory medicine by providing essential diagnostic and prognostic information for neurological, infectious, inflammatory, and neoplastic diseases. However, the interpretation of these specimens remains challenging because it requires the integration of heterogeneous biochemical, cytological, microbiological, molecular, and clinical data, often in the absence of standardized analytical workflows. Artificial intelligence (AI), particularly Machine Lea…

A systematic review of artificial intelligence in small ruminant production systems: applications, performance outcomes, and reported implementation challenges.

BACKGROUND: Sheep and goats are essential components of global livestock systems, supporting smallholder livelihoods and contributing substantially to meat, milk, and fiber production. However, many small ruminant production systems remain extensive and constrained by limited infrastructure, labor availability, and challenges in continuous animal monitoring. Artificial Intelligence (AI) offers new opportunities for precision livestock management through automated data analysis, early detection of production- and health-related changes, and improved decision support. This systematic review aime…

Artificial intelligence vs human opportunistic detection of vertebral fractures in routine CT scans: results of a pilot study.

PURPOSE: Vertebral fractures (VFs) are the most common osteoporotic fractures and a hallmark of bone fragility, yet most morphometric VFs remain undiagnosed in routine imaging not focused on the spine. This pilot study evaluated the diagnostic performance of an artificial intelligence (AI) system for opportunistic vertebral fracture detection in chest and abdominal computer tomography (CT) scans compared with human readers in a real-world hospital setting. METHODS: Over 2 months, all thoracic and abdominal CTs performed for any indication in patients aged ≥ 50 years at a tertiary hospital were…

Evaluation of ergonomic work postures among dental students during restorative procedures using an artificial intelligence-based assessment model.

BACKGROUND: Dental professionals are frequently exposed to work-related musculoskeletal disorders (WMSDs), with ergonomic risks often emerging during undergraduate dental training. While most existing studies rely on self-reported questionnaires, objective evaluations of work postures remain limited. This study aimed to evaluate work postures adopted by dental students while performing restorative procedures in simulation laboratories using an AI-based assessment model. METHODS: A cross-sectional analytical study was conducted among dental students. Participants were recruited through a non-p

Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis.

Artificial intelligence (AI) is an accurate screening tool for diabetic retinopathy (DR), the leading cause of blindness among working-aged adults. However, its impact on referral uptake is uncertain. We searched Embase, MEDLINE, Scopus, Web of Science and Cochrane Library databases from year 2000 to February 17, 2025. Randomised and non-randomised studies comparing referral uptake after AI-assisted DR screening versus standard of care were included. 2644 articles were identified, and six included for analysis. The relative risk of DR referral uptake with AI-assisted screening compared with th…

Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.

Background and Objectives: Bile duct injury remains a major safety concern during laparoscopic cholecystectomy, and reliable interpretation of hepatocystic anatomy is fundamental to safe dissection. Artificial intelligence (AI)-based computer vision may support anatomical recognition, critical view of safety (CVS) assessment, and intraoperative decision support. This narrative review synthesized the current evidence, clinical applications, readiness for implementation, and future requirements of anatomy-aware AI during laparoscopic cholecystectomy. Materials and Methods: Five bibliographic dat…

Diagnostic Accuracy of Artificial Intelligence Systems in Early Prediction of Dislocation and Aseptic Loosening After Total Hip Arthroplasty.

BACKGROUND: Artificial intelligence (AI) is increasingly used in total hip arthroplasty (THA), yet its role in postoperative imaging remains limited. This study evaluated the diagnostic performance of AI systems in interpreting postoperative radiographs following THA. METHODS: A total of 1,045 patients who underwent primary THA between 2011 and 2015 were retrospectively analyzed. The first-day postoperative anteroposterior pelvic radiographs were assessed using AI systems to estimate the risk of dislocation and aseptic loosening. During a mean follow-up of 11.6 years (range, 10 to 14), 34 pat…

Искусственный интеллект, видоизменяющий парадигму гематологического диагноза и лечения злокачественных заболеваний: от статической оценки до динамического управления точностью.

Искусственный интеллект (ИИ) все чаще изучается в качестве инструмента содействия более точному и динамичному управлению диагностикой и лечением гематологов. В отличие от предыдущих обзоров, в которых основное внимание уделялось отдельным типам заболеваний или изолированным технологическим путям, в настоящем документе содержится всеобъемлющий обзор текущих прикладных программ АИШ и последних достижений в области диагностики, классификации, оценки прогнозов и принятия решений по лечению лейкемии, лимфомы, множественной миэломы и миэлодиспластических синдромов. Она охватывает ключевые технически

Совпадение в аннотации оригинала
Artificial intelligence (AI) is increasingly being explored as a tool to support more precise and dynamic management in the diagnosis and treatment of hematologic malignancies. Unlike previous reviews focused on single disease types or isolated technological pathways, this paper provides a comprehensive overview of AI’s current applications and latest advanc…

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.

BACKGROUND: Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs). Early identification of high-risk patients is needed. This proof-of-concept study evaluated whether machine learning (ML) with explainable AI (SHAP) could predict EV toxicities using real-world data. RESEARCH DESIGN AND METHODS: Data from 542 patients (51 centers, 24 countries) were analyzed. Six outcomes were predicted including grade 3-4 AEs. Four ML algorithms were trained on an 80% split and tested on 20%. Performance was evaluated via standard metrics with SHAP for interpreta

Artificial intelligence-guided discovery of fungal ergosterol derivatives as selective LXRβ agonists targeting the cholesterol dependency of glioblastoma.

Glioblastoma (GBM) is the most lethal primary brain malignancy, for which no new systemic therapy has achieved regulatory approval in over p … ated with LXRα co-activation has remained an unresolved challenge. Here, we developed an integrated artificial intelligence-based virtual screening pipeline. The pipeline integrates ML-QSAR modelling, D-MPNN, and deep learning-based DTI prediction. It was applied to systematically interrogate a curated library of ~ 1.2 million natural products. Sequential filtering by pr …

Bibliometric Analysis of Whole-Body MRI from 2015 to 2025 Across Clinical Applications, Quantitative Imaging, and Artificial Intelligence.

Whole-body magnetic resonance imaging (WB-MRI) has become established for selected clinical indications and is increasingly studied across many fields including but not limited to oncologic, musculoskeletal, pediatric, and computational imaging applications. This study characterizes recent trends and the evolution of WB-MRI research using a bibliometric analysis reported using the PRISMA 2020 guidelines and identifies dominant clinical, quantitative, and artificial intelligence (AI) themes shaping the field. Publications were identified from Scopus, Web of Science, and PubMed using WB-MRI and …

Artificial Intelligence impact on decision-making regarding short-term ADT with prostate radiotherapy: ASTuTE clinical trial interim results.

This interim analysis of a multicentre implementation trial (n = 200) evaluated a multimodal artificial intelligence biomarker's impact on shared short-term androgen deprivation therapy (ST-ADT) decisions in intermediate-risk prostate cancer (IR-PC). Biomarker testing altered 27.5% (CI: 21.4-34.2%) of final decisions. ST-ADT use significantly decreased: 70.3% (CI: 58.5-80.3%) of patients initially electing ST-ADT changed to 'NO', versus a 2.4% (CI: 0.5-6.8%) shift to 'YES' (p < 0.001). Decision changes were significantly greater in unfavourable versus favourable IR-PC (35% v 4%; p < 0.001). Th…