Rheumatology

Latest AI and machine learning research in rheumatology for healthcare professionals.

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Showing 1461-1480 of 5,622 articles

Early Prediction of Anti-PD-1 Therapy Response in Hepatocellular Carcinoma Using Gut Microbiota Biomarkers

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, and response rates to anti-PD-1 therapy are suboptimal. Previous models predicting therapy response often fail to provide reliable early predictions, limiting their clinical utility. To develop a robust predictive model based on gut microbiota features that can accurately discriminate between responders (R) and non...

Optimizing the Clinical Application of Rheumatology Guidelines Using Large Language Models: A Retrieval-Augmented Generation Framework Integrating EULAR and ACR Recommendations

Timely access to current rheumatology guidelines at the point of care is challenging. We aimed to develop and evaluate the first Retrieval-Augmented Generation (RAG) system specifically designed for adult rheumatology, integrating European Alliance of Associations for Rheumatology (EULAR) and American College of Rheumatology (ACR) guidelines to provide rheumatologists with timely, evidence-based r...

AI-detected tumor-infiltrating lymphocytes for predicting outcomes in anti-PD1 based treated melanoma

Easy and accessible biomarkers to predict response to immune checkpoint inhibition (ICI)-treated melanoma are limited. To evaluate artificial intellig...

Interpretable Machine Learning based Detection of Coeliac Disease

Coeliac disease, an autoimmune disorder affecting approximately 1% of the global population, is typically diagnosed on duodenal biopsy. However, inter...

Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development

Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provi...

SLOTMFound: Foundation-Based Diagnosis of Multiple Sclerosis Using Retinal SLO Imaging and OCT Thickness-maps

Multiple Sclerosis (MS) is a chronic autoimmune disorder of the central nervous system that can lead to significant neurological disability. Retinal i...

Evaluating anti-LGBTQIA+ medical bias in large language models

Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While th...

Adapting Biomedical Foundation Models for Predicting Outcomes of Anti Seizure Medications

Epilepsy affects over 50 million people worldwide, with anti-seizure medications (ASMs) as the primary treatment for seizure control. However, ASM sel...

Predicting coronary artery abnormalities in Kawasaki disease: Model development and external validation

Kawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 pos...

Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease ...

Toward Reliable Thalamic Segmentation: a rigorous evaluation of automated methods for structural MRI

Automated thalamic nuclear segmentation has contributed towards a shift in neuroimaging analyses from treating the thalamus as a homogeneous, passive ...

Discriminating the prodromal stage of multiple sclerosis using longitudinal health administrative claims data and machine learning–based sequence analysis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. Early detection of the prodromal phase could enable timely inte...

Optimizing Dose-Response Decisions in Psoriatic Arthritis via Causal Machine Learning: A Real-World Evaluation of Secukinumab Treatment

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their firs...

Metformin use is associated with lower mortality from bacterial sepsis and improved immunocompetence in Thai diabetes patients with acute melioidosis

Diabetes mellitus (DM) is a major risk factor for acquiring infections. Metformin, the first-line treatment for type 2 DM, is associated with benefici...

A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts

Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...

Arachnoiditis: Leveraging crowdsourcing and AI in a cross-sectional study of 1,105 cases to improve identification, understanding, and treatment

Arachnoiditis, a painful and potentially disabling neurological condition, results from persistent inflammation of the spinal cord pia-arachnoid membr...

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWAS

MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but their availability is limited by cost and throughpu...

A Tabular Residual Neural Network for Diabetes Classification and Prediction

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with type 1 characterized as an autoimmune destruction of pancreatic be...

autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis

Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis (RA). Scoring system...

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