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 CHA₂DS₂-VASc. Finally, AI has been increasingly integrated into procedural planning and execution for AF ablation, helping to identify optimal ablation targets and predict post-procedural arrhythmia recurrence risk for a given rhythm control strategy, based on imaging and biosignal-derived features. In summary, the emerging integration of machine learning approaches into AF management highlights its transformative potential to offer earlier detection, more precise and personalized risk stratification, and tailored therapeutic strategies and patient follow up. Despite these advancements, the clinical implementation of AI in AF management remains primitive, requiring large-scale validation, supplemental clinical oversight, and regulatory guidance to ensure safe and effective integration into our daily practices.