Artificial Intelligence Medical Compendium

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

Showing 5,871 to 5,880 of 205,623 articles

Two decades of human- and climate-induced groundwater storage shifts in Brazil.

Science advances
Brazil holds the world's largest reserves of renewable fresh water, yet recurrent water crises expose its growing vulnerability under extreme events. As the nation's groundwater demand increases, these reserves still are poorly monitored. Here, we pr... read more 

Implementation of the EPIC Deterioration Index Tool on a Medical/Surgical Unit.

Clinical nurse specialist CNS
PURPOSE: Early detection is crucial for preventing clinical deterioration. This quality improvement project aimed to investigate the application of a machine-based learning tool in the medical/surgical setting. DESCRIPTION: This quality improvement p... read more 

Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the modified NUTRIC (mNUTRIC) score often rely on subjective assessments or delayed data, limiting their... read more 

When Timing Matters: Evaluating Temporal Leakage in Machine Learning Models of Football Pass Turnovers.

Research quarterly for exercise and sport
The Expected Pass Turnovers (xPT) model advances turnover probability quantification in professional football, but the inclusion of post-pass descriptive features such as ball speed and distance moved introduces temporal leakage and limits real-time ... read more 

Moving From Keywords to Contextual Meaning: A Commentary on Hybrid Bibliometric Synthesis in Health Research.

Journal of medical Internet research
The fast growth of social media mining in health research has contributed to an invaluable but quite fragmented body of literature. As the amount of unstructured patient-reported data grows, traditional bibliometric analyses face methodological limit... read more 

Clinical Evaluation of the Clinical Reasoning Process of Large Language Models in Nephrology: Comparative Evaluation Study.

JMIR formative research
This study evaluates the dynamic clinical reasoning of 4 leading large language models in complex nephrology cases, demonstrating that while Gemini 2.5 Pro achieved the highest reasoning scores and computational efficiency, all tested models excelled... read more 

Suicidal Ideation in Online Spaces Through the Lens of Interpersonal Theory of Suicide: Exploratory Study of Self-Disclosure, Peer Support, and AI Responses.

JMIR AI
BACKGROUND: Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Global estimates suggest that the lifetime prevalence of SI ranges between 9% and 12% worldwide, underscoring the scale of this... read more 

Forecasting the Impacts of Artificial Intelligence Assistance in Virtual Consultations for Chronic Obstructive Pulmonary Disease: Exploratory Futures Wheel Study.

Journal of medical Internet research
BACKGROUND: While digital health technologies promise to reshape the medical journey, their potential might not be realized due to unforeseen implementation challenges. Notably, the future impact of artificial intelligence (AI) in virtual consultatio... read more 

Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.

JMIR diabetes
BACKGROUND: Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of glucose metabolism. Traditional static insulin regimens fail to adapt to individual variability in ... read more 

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering.

IEEE transactions on pattern analysis and machine intelligence
Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from heterogeneous and limited data. To alleviate the degradation, we prese... read more