Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 43,811 to 43,820 of 224,055 articles

AI-Driven Feature Selection Using Only Survey Variable Descriptions: Large Language Models Identify Adolescent Vaping Predictors

medRxiv Mar 9, 2026
Objective: To evaluate the effectiveness of various Large Language Models (LLMs) in identifying reliable predictors of Electronic Nicotine Delivery Systems (ENDS) initiation among adolescents, using solely large-scale survey variable descriptions. Me... read more 
health informatics

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

medRxiv Mar 9, 2026
Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet heterogeneous electronic health record (EHR) data, class imbalance, and evolving clinical practice prese... read more 
health informatics

Extracting patient reported cannabis use and reasons for use from electronic health records: a benchmarking study of large language models

medRxiv Mar 9, 2026
Objective To develop and evaluate a scalable and reproducible natural language processing (NLP) approach using large language models (LLM), to identify cannabis use status and reasons for cannabis use among patients with autoimmune rheumatic diseases... read more 
health informatics

Predicting Graduation in Undergraduate Medical Education: A Machine Learning Analysis Across Diverse High School Curricula

medRxiv Mar 9, 2026
ABSTRACT Background : The United Arab Emirates (UAE) is characterised by a diverse educational landscape, where students enter medical school from various high school curricula. Understanding how these varied academic backgrounds influence medical st... read more 
medical education

Gait-Related Digital Mobility Outcomes in Parkinson's Disease: New Insights into Convergent Validity?

medRxiv Mar 9, 2026
Objective: In Parkinson's disease (PD), gait-related digital mobility outcomes (DMOs) show promise for monitoring mobility decline, but convergent validity remains limited. To improve convergent validity, demonstrating convergence with motor severity... read more 
neurology

Impact of Image Bit Depth Reduction on Deep Learning Performance in Chest Radiograph Analysis: A Multi-institutional Study

medRxiv Mar 9, 2026
Purpose Medical imaging typically generates 12- to 16-bit formats, yet conversion to 8-bit is often required. While deep learning has been widely explored in medical imaging, the influence of image bit depth on model performance is not fully understo... read more 
radiology and imaging

Geometric Brain Signatures for Diagnosing Rare Hereditary Ataxias and Predicting Function

medRxiv Mar 9, 2026
Hereditary cerebellar ataxias (HCAs) are rare neurodegenerative disorders characterised by progressive motor impairment and overlapping clinical phenotypes. Although genetic testing provides etiological diagnosis, diagnostic delays frequently arise b... read more 
neurology

Condition-Specific Readmission Risk Stratification in a Predominantly Black Statewide Cohort Using Machine Learning: Development of Subtype-Specific Models for Heart Failure, Acute Myocardial Infarction, Atrial Fibrillation/Flutter, and Hypertensive Heart Disease

medRxiv Mar 9, 2026
Background: Cardiovascular disease (CVD) readmissions impose substantial clinical and economic burden. Machine learning (ML) may improve risk stratification, yet most predictive models aggregate CVD subtypes into a single outcome and underrepresent B... read more 
cardiovascular medicine

Relationship Between Gene Expression and Drug Response in Triple-Negative Breast Cancer: Leveraging Single-Cell RNA Sequencing and Machine Learning to Identify Biomarker Profiles

bioRxiv Mar 8, 2026
Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited therapeutic options and poor prognosis. To address these challenges, we combined single-cell RNA sequencing (scRNA-seq) data with advanced machine learning techniq... read more 
bioinformatics

REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning

bioRxiv Mar 8, 2026
Metagenome-assembled genomes (MAGs) are central to exploring microbial communities. Yet, despite the relevance of protists and fungi to diverse ecosystems, eukaryotic MAG recovery lags behind that of prokaryotes. A major bottleneck is that most state... read more 
bioinformatics
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