Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy

Abstract

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, associated with significant morbidity, mortality, and healthcare burden. Despite advances in AF management, challenges persist in early detection, risk stratification, and treatment optimization, necessitating innovative solutions. Artificial intelligence (AI) has emerged as a transformative tool in AF care, leveraging machine learning and deep learning algorithms to enhance diagnostic accuracy, improve risk prediction, and guide therapeutic interventions. AI-powered electrocardiographic screening has demonstrated the ability to detect asymptomatic AF, while wearable photoplethysmography-based technologies have expanded real-time rhythm monitoring beyond clinical settings. AI-driven predictive models integrate electronic health records and multimodal physiological data to refine AF risk stratification, stroke prediction, and anticoagulation decision making. In the realm of treatment, AI is revolutionizing individualized therapy and optimizing anticoagulation management and catheter ablation strategies. Notably, AI-enhanced electroanatomic mapping and real-time procedural guidance hold promise for improving ablation success rates and reducing AF recurrence. Despite these advancements, the clinical integration of AI in AF management remains an evolving field. Future research should focus on large-scale validation, model interpretability, and regulatory frameworks to ensure widespread adoption. This review explores the current and emerging applications of AI in AF, highlighting its potential to enhance precision medicine and patient outcomes.

Keywords: artificial intelligence; atrial fibrillation; catheter ablation; electroanatomic mapping; electrogram analysis; machine learning; predictive modeling.

Figure 1

The role of artificial intelligence (AI) in atrial fibrillation (AF) management is expanding, with advancements in risk stratification, personalized medical therapy, and catheter ablation strategies. AI-driven electrocardiographic and imaging models have shown promise in improving stroke risk prediction compared to conventional clinical scores. Machine learning (ML) algorithms contribute to refining neurological deterioration risk assessment in AF-related stroke and assist in predicting cardioversion success. In personalized AF management, AI is being applied to optimize anticoagulation and antiarrhythmic therapy by improving drug dosing, supporting medication adherence, and enabling real-time monitoring. In catheter ablation, AI-powered electroanatomic mapping, recurrence risk prediction, and real-time procedural guidance are being explored to enhance procedural precision and outcomes. While these advancements are promising, challenges remain in integrating AI into clinical practice. Further validation, regulatory considerations, and real-world implementation studies are needed to support broader adoption. AI has the potential to improve AF diagnosis, risk assessment, and treatment decisions, contributing to a more data-driven and individualized approach to AF management.