The adoption of machine learning approaches in systematic reviews is fundamentally transforming evidence-based medicine. Traditionally, systematic reviews have involved painstaking manual screening of ...

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...

According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...

A multi-institutional research team has demonstrated how artificial intelligence and machine learning can optimize therapy selection and dosing for septic shock, a life-threatening complication that ...

Cui, J.X., Liu, K.H. and Liang, X.J. (2026) A Brief Discussion on the Theory and Application of Artificial Intelligence in ...

From wearables for health monitoring and self-care apps, to machine learning analysis of medical images, the potential of digital technologies to revolutionise healthcare has commanded many headlines.

Metabolite data and AI combine to redefine how we measure aging and predict health spans. Study: Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

Key market opportunities in camera image signal processors include optimizing AI-driven processing for diverse conditions, meeting demands in automotive, medical, and smartphone sectors, and ...