Artificial intelligence in atrial fibrillation - Timely diagnosis, risk assessment and personalized management.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity and mortality, including stroke, systemic embolism, heart failure, and dementia. Timely diagnosis, accurate risk stratification, and personalized management are necessary to improving outcomes. Recent advancements in artificial intelligence (AI) have expanded the potential for AF care, leveraging machine and deep learning approaches for enhanced detection, risk assessment, and therapeutic guidance. In this review, we summarize the clinical integration of AI into AF management across three domains. First, AI-enhanced electrocardiography (ECG) and wearable photoplethysmography devices allow early detection and long-term, non-invasive screening of AF, including identification of subclinical or paroxysmal AF from routine sinus rhythm recordings. Second, AI models have the potential to refine stroke risk stratification and personalize anticoagulation decision-making by integrating multidimensional clinical data, providing individualized risk assessments beyond traditional scoring systems like CHA