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A multimodal artificial intelligence method for accurate diagnosis of autoimmune gastritis.

BACKGROUND: Autoimmune gastritis (AIG), a chronic inflammatory disease associated with various comorbidities and complications, is often subject to missed or delayed diagnoses. AIMS: The objective of this study was to develop an artificial intelligence (AI) system to assist in the diagnosis of AIG by integrating multimodal information, including endoscopic images, biopsy pathological reports, and laboratory test results. METHODS: Multimodal data were collected from 590 patients diagnosed with AIG, H. pylori positive, and H. pylori negative chronic atrophic gastritis at three medical centers.…

Characterizing the clinical profile of primary headache using explainable artificial intelligence.

Background/HypothesisDifferentiating primary from secondary headaches remains challenging due to overlapping symptoms and the subjective nature of pain assessment. Machine learning (ML) combined with explainable artificial intelligence (XAI) offers a promising approach for improving diagnostic accuracy while maintaining clinical interpretability. The study aimed to develop an interpretable ML model to differentiate primary from secondary headaches and to identify the relative diagnostic contributions of individual clinical features.MethodsThis cross-sectional study analyzed 1219 patients using…

A Novel Chaos-inspired Artificial Intelligence-based Model for Keratoconus Prediction.

PURPOSE: To develop a novel chaos-inspired artificial intelligence (AI)-based system for keratoconus prediction. METHODS: We constructed, trained, and tested our prediction model using corneal tomography data obtained from Pentacam and Sirius imaging systems. The study included 65 healthy individuals and 48 patients with keratoconus recruited from a private ophthalmology clinic and Bina Eye Hospital in Tehran, Iran. Model development included stratified partitioning of the data into training and testing sets, followed by performance evaluation using standard classification metrics. Scheimpflu…

Clinically relevant evidence on imaging and artificial intelligence in hidradenitis suppurativa.

This review provides a comprehensive analysis of the role of imaging and artificial intelligence in hidradenitis suppurativa (HS), highlighting the strengths and limitations of each modality and providing the level of evidence for each imaging technique. To date, ultrasound has emerged as the only modality with validated diagnostic criteria, standardized scoring systems and strong international multicentre evidence and consensus, supporting its role as the first-line tool for diagnosis, staging, pre-surgical mapping and treatment guidance in HS. Other techniques, such as dermoscopy, magnetic r…

Artificial Intelligence-Empowered Single-Cell Phenotyping for Rapid, Automated Pathogen Diagnostics.

Accurate bacterial identification and antimicrobial susceptibility testing (AST) are essential for timely clinical decision-making. Conventional diagnostic methods typically require 24-48 h, leading to inappropriate empirical therapy, accelerating antimicrobial resistance, and worsening patient outcomes. Here, we present an integrated diagnostic platform that combines microfluidic single-cell bacterial detection with artificial intelligence (AI)-driven analysis for rapid phenotypic AST. The microfluidic device enables stable time-lapse detection of individual bacterial cells under designated, …

Automated Detection of Tuberculosis on Chest X-Rays Using Artificial Intelligence.

Detecting tuberculosis on X-rays remains complex and challenging for clinicians. In Burkina Faso, the scarcity of radiologists and their heavy workload increase the risk of misinterpretation, particularly in areas with high demand. To address this constraint, we have designed a comprehensive solution based on artificial intelligence, ranging from data analysis to the training of advanced deep learning models, enabling the automatic identification of tuberculosis abnormalities from chest X-rays. In this article, we present our three-step approach for tuberculosis detection, directly inspired by…

Artificial intelligence in advanced endoscopic imaging: transforming optical diagnosis in gastroenterology.

The term Artificial intelligence (AI) is revolutionizing gastrointestinal (GI) endoscopy by enhancing advanced imaging techniques such as Narrow Band Imaging (NBI), Linked Color Imaging (LCI), iSCAN, and Confocal Laser Endomicroscopy (CLE). AI-driven deep learning algorithms, particularly convolutional neural networks (CNNs) and transformer-based models, have demonstrated high accuracy in the real-time detection, classification, and risk stratification of premalignant and malignant lesions, thereby reducing unnecessary biopsies and improving diagnostic efficiency. In the upper GI tract, AI has…

Use of artificial intelligence in building personal branding and intercultural leadership.

The integration of AI tools reshaping how professionals learn, build reputation, and project their value in digitally and culturally diverse … echnology adoption forming the central dimensions of the model. The results suggest that the use of artificial intelligence tools is associated with better career prospects when accompanied by critical judgment, authenticity, and cultural sensitivity. They also highlight the need to develop digital, ethical, and intercultural competencies in universities and organization …

From theory to therapy: integrating artificial intelligence for transformative healthcare innovation.

The rapid evolution of artificial intelligence (AI) is reshaping healthcare by improving diagnostics, patient outcomes, and operational efficiency. Yet, many frameworks for AI adoption overlook the complex and iterative nature of healthcare systems. This study introduces the AI Healthcare Symbiosis Cycle (AI-HSC), a novel framework based on Dynamic Capabilities Theory, Systems Theory, and Kotter's 8-Step Change Model, conceptualising AI adoption as a continuous and adaptive process. Dynamic Capabilities Theory highlights the need for organisations to sense opportunities, seize resources, and c…

Radiomics and Artificial Intelligence in Multiple Sclerosis MRI: A Comprehensive Review.

Radiomics is a process of extracting quantitative features from medical images, such as MRI. This process, combined with artificial intelligence (AI), has already been investigated in several studies on the management of MS. The aim of this review article was to provide an overview of the various applications of MRI radiomics in the diagnosis and prognosis of MS. The literature search was conducted in PubMed and Scopus for articles published between 2015 and 2025. A total of 26 articles met the specified criteria. Studies found that radiomics features from brain MR images, combined with AI mod…

Utility of artificial intelligence for identifying thoracic lymphadenopathy in lung cancer.

PURPOSE: To evaluate the performance of an artificial intelligence (AI) system for detecting, classifying, and measuring AI-detected thoracic lymph node candidates on CT and compare AI detection with routine radiology reporting and histopathologic findings. METHODS: In this HIPAA-compliant, IRB-approved multicenter study, 169 patients with operable thoracic malignancies underwent invasive mediastinal or hilar lymph node sampling and chest CT within 6 weeks (mean, 16 days ± 10). Patients were identified using a query of the EPIC database. CT images were processed using a commercially available…

Искусственный интеллект в педиатрической нейрорадиологии: текущий ландшафт, проблемы и будущие направления.

В настоящем обзоре содержится информация о нынешнем ландшафте искусственного интеллекта (АИ) в области педиатрической и эмбриональной нейрорадиологии, критическая оценка текущей практики, барьеров на пути к клиническому усыновлению и будущего потенциала. Мы искали рецензированные исследования за последнее десятилетие, сфокусировавшись на сегментации изображений, обнаружении повреждений, классификации, прогнозировании и поддержке клинического решения при педиатрической съемке мозга. Особое внимание было уделено уникальным педиатрическим факторам, таким, как развитие мозга и нехватка данных. Мет

Совпадение в аннотации оригинала
This narrative review maps the current landscape of artificial intelligence (AI) in paediatric and fetal neuroradiology, critically evaluating current practice, barriers to clinical adoption, and future potential. We searched for peer-reviewed studies from the last decade, focusing on image segmentation, lesion detection, classification, prognostication, and…

Artificial intelligence in rehabilitation: a living systematic mapping review - first release.

INTRODUCTION: Artificial intelligence (AI) is revolutionizing many fields of science, including medicine, by enhancing diagnostic, therapeutic, and decision-making processes. However, understanding how AI can be truly useful in everyday clinical practice remains challenging. This living review systematically investigates current clinical applications of AI in the rehabilitation field, clarifies key concepts, and identifies knowledge gaps. EVIDENCE ACQUISITION: PubMed, Scopus, WOS/Embase, and PEDro were systematically searched from December 1, 2014, to December 1, 2024. Retrieved articles were…

Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.

Myelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smears, multiparametric flow cytometry data, and clinical patient information is part of the current, time-consuming, and labor-intensive work up for myelodysplastic syndromes. Nowadays, clinical biomedical research has been transformed by the advent of artificial intelligence, specifically machine lear…

Integrating tumor location into artificial intelligence-based prognostic models in cancer.

This letter is a commentary on the findings of Huang et al, who emphasize the prognostic value of tumor location in gastric cancer. Analyzin … dels for various cancers. This letter highlights the potential of incorporating tumor location into artificial intelligence -based prognostic tools to enhance prognostic models. It also outlines a stepwise framework for developing these models, from retrospective training to prospective multicenter validation and clinical implementation. In addition, it addresses the tec …

Applicability of Artificial Intelligence Analysis in Oral Cytopathology: A Pilot Study.

INTRODUCTION: Oral cancer, especially oral squamous cell carcinoma (OSCC), is a global health challenge due to factors such as late detection and high mortality rates. Early detection is essential through monitoring by healthcare professionals. Cytopathology is a cellular analysis model for evaluating cellular damage preceding the clinical appearance of OSCC, but it requires training and has diagnostic limitations, due to its subjective aspect. Artificial intelligence (AI) shows potential to enhance the interpretation of cytological images, reducing working time and subjectivity. OBJECTIVE: T…

Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.

BACKGROUND: Artificial intelligence (AI) is rapidly expanding in ophthalmology, yet its adoption in daily practice remains limited. Understanding clinicians’ perspectives is essential to address barriers and guide targeted education. METHODS: We conducted a cross-sectional international survey of licensed ophthalmologists from 45 countries across all continents between October 2024 and February 2025. The questionnaire evaluated AI familiarity and use as primary outcomes, as well as perceived clinical impact, ethical concerns, and training preferences of participants. Descriptive and comparativ…

Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine.

The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a data-driven, precision discipline. This review synthesizes recent advances in AI-enabled outcome prediction techniques, encompassing deep learning, meta-analytic modeling, natural language processing (NLP), computer vision, and neuroimaging-based analysis. For instance, convolutional neural networks (CNNs) have been successfully applied to classify tongue images and detect ZHENG patterns, while transformer-based NLP models enable aut…

Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.

Abdominopelvic trauma is a major cause of morbidity and mortality, typically resulting from high-energy mechanisms such as motor vehicle col … ay radiology report turnaround times, which can potentially impede urgent clinical decision-making. Artificial intelligence (AI) computer-aided detection and diagnosis (CAD) offers promising solutions for enhanced diagnostic efficiency and accuracy in abdominopelvic trauma imaging. Although commercial AI tools for abdominopelvic trauma are currently available for only a …

Artificial Intelligence in Endoscopic and Ultrasound Imaging for Inflammatory Bowel Disease.

Artificial intelligence (AI) is rapidly transforming imaging modalities in inflammatory bowel disease (IBD), particularly in endoscopy and ultrasound. Despite their critical roles, both modalities are challenged by interobserver variability, subjectivity, and accessibility issues. AI offers significant potential to address these limitations by enhancing lesion detection, standardizing disease activity scoring, and supporting clinical decision-making. In endoscopy, deep convolutional neural networks have achieved high accuracy in detecting mucosal abnormalities and grading disease severity, red…