Artificial Intelligence Medical Compendium

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

Showing 10,131 to 10,140 of 208,952 articles

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
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
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
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
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
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
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
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
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 

CNNs vs. transformers: A benchmark for multi-class marine debris identification.

The Science of the total environment
Marine debris presents serious threats to aquatic ecosystems, biodiversity, and human livelihoods, with most carrying plastic wastes. This study addresses the urgent need for effective marine debris classification through advanced deep learning model... read more 

Elucidating the pathogenic mechanism underlying plasticizer-induced chronic kidney disease via integrated network toxicology and machine learning.

Ecotoxicology and environmental safety
Plasticizers are ubiquitous plastic additives that have been linked to multiple adverse health outcomes and elevated risks of chronic kidney disease (CKD). However, the precise molecular mechanisms underlying plasticizer-induced CKD remain poorly und... read more