AI Medical Compendium

Ambiguity Detection in Medical Exams via Large Language Models: Retrospective Cross-Sectional Pilot Study.

JMIR medical education May 26, 2026

BACKGROUND: Large language models (LLMs) have emerged as promising tools in medical education due to their ability to understand, generate, and reason with natural language. Their ability to simulate expert reasoning suggests a potential for supporti... read more

Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm Validation.

Journal of medical Internet research May 26, 2026

BACKGROUND: The application of generative artificial intelligence to simplify medication use instructions has the potential to enhance people's health by improving treatment adherence. OBJECTIVE: We evaluated the performance of large language models ... read more

Exploring the Use of Artificial Intelligence and Wearable Technologies in the Context of Cardiovascular Prevention From Early Detection to Cardiac Recovery: Protocol for a Scoping Review.

JMIR research protocols May 26, 2026

BACKGROUND: Cardiac rehabilitation is an evidence-based, multidisciplinary intervention integrating therapeutic exercise, patient education, nutritional counseling, optimized pharmacological management, and psychological support. It reduces cardiovas... read more

Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.

JMIR cancer May 26, 2026

BACKGROUND: Despite a global decline in the incidence of gastric cancer (GC), the number of cases diagnosed among younger individuals continues to increase. Several studies have been conducted to develop predictive models of mortality in patients wit... read more

Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis.

JMIR medical education May 26, 2026

BACKGROUND: Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends-technological upheaval and ongoing mental health iss... read more

Survival prediction modeling for 1-year mortality in patients with ST elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).

Heart & lung : the journal of critical care May 26, 2026

BACKGROUND: ST elevation myocardial infarction (STEMI) is a life-threatening condition, and is associated with significant mortality, especially in patients encountering cardiogenic shock. Accurate risk assessment in this population is essential for ... read more

From filtering to denoising: Increasing visual interpretability of cryo-electron tomograms.

Current opinion in structural biology May 26, 2026

Cryo-electron tomography has emerged as the premier technique for ultrastructural analysis of natively preserved biological specimens and in situ structure determination. Each tomogram of a cell contains valuable information on the imaged molecular a... read more

An efficient methodology for modeling imbalanced traffic crashes through deep learning techniques.

Accident; analysis and prevention May 26, 2026

Accurately predicting crash injury severity is crucial for enhancing traffic safety. However, crash datasets are often highly imbalanced because severe injuries are infrequent, which can bias predictive models. Addressing these challenges, the presen... read more

From design-build-test-learn cycles to AI-driven digital twins for bioprocess scale-up in the Genesis Mission era.

Current opinion in biotechnology May 26, 2026

The Genesis Mission is a U.S. initiative to accelerate bioproduction by integrating synthetic biology with the artificial intelligence (AI) ecosystem. However, it also raises caution regarding AI-driven biotechnology. Biomanufacturing requires the co... read more

Addendum to "Predicting treatment pathways in Class II malocclusion patients using machine learning: A comparative study of four algorithms for classifying camouflage, growth modulation, and surgical decisions" [Int Orthod. 24 (2026) 101070].

International orthodontics May 26, 2026

OBJECTIVES: The aim of this study was to develop a machine learning model to assist in treatment decision-making for surgery, camouflage, and growth modulation in Class II malocclusion patients and to evaluate its validity and reliability. MATERIAL A... read more

Artificial Intelligence Medical Compendium