AI-Driven Atrial Fibrillation Management: From Signal to Strategy

Abstract

Abstract Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity, including ischemic stroke, heart failure, and cognitive decline. Despite established diagnostic and therapeutic strategies, AF remains frequently underdiagnosed and suboptimally managed, particularly in asymptomatic or paroxysmal cases, in which the episodic nature of the arrhythmia may make it difficult to detect using standard electrocardiography. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a transformative technology across multiple aspects of AF care. AI-based electrocardiographic analysis and wearable technologies have demonstrated promising performance in detecting subclinical AF and facilitating scalable population screening. Multimodal models integrating clinical, imaging, and electrophysiological data have demonstrated improved accuracy in stroke prediction, recurrence risk estimation, and therapeutic planning. AI-assisted imaging has advanced atrial segmentation, fibrosis characterization, and ablation planning, whereas AI-driven mapping systems may enhance procedural efficiency and improve patient selection. Nevertheless, significant challenges remain, including limited external validation, data heterogeneity, concerns regarding interpretability, integration into clinical workflows, and ethical and regulatory considerations. Future efforts should prioritize explainable, clinically validated, and human-centered AI systems that are integrated into real-world clinical workflows. This review provides a comprehensive overview of current AI applications in AF management, including early detection, risk stratification, cardiovascular imaging, clinical decision support, and interventional electrophysiology.