关键词: 心房颤动, 人工智能, 心电图, 深度学习, 临床应用, 健康公平

Abstract:

Atrial fibrillation （AF） is one of the most common arrhythmias encountered in clinical practice， and its population-level disease burden is growing globally， while conventional management strategies have multiple limitations. Artificial intelligence （AI） has opened new technological avenues for the comprehensive management of AF. This narrative review summarizes the current applications， recent research advances of AI in the comprehensive management of AF， as well as the key challenges for its clinical translation. Specifically， AI-enhanced electrocardiography （ECG） integrated with wearable devices enables opportunistic screening across diverse clinical settings； AI-assisted fully automated ECG analysis achieves accuracy and efficiency that match or even exceed those of conventional manual analysis； AI-based predictive models outperform traditional clinical risk scoring systems in predicting the risk of new-onset AF， thromboembolic events， and recurrence after catheter ablation， although improvements in bleeding risk prediction remain limited； AI-assisted personalized anticoagulation， intraprocedural ablation navigation， and clinical decision-making for rhythm control versus rate control have all demonstrated promising clinical application potential. However， the clinical translation of AI for AF management still faces multiple challenges at the technical， practical application， and policy levels. From a technical perspective， the most prominent issues include insufficient model interpretability， widespread data silos， inadequate multimodal data fusion， the gap between model performance and clinical utility， and noise interference in data collection. In terms of practical application， real-world implementation is constrained by fragmented healthcare information systems， superficial integration of AI tools into existing workflows， delayed coverage of AI-related technologies by medical insurance reimbursement， limited AI-related capabilities in primary healthcare settings， the absence of closed-loop disease management systems enabled by AI， and the existing digital divide. At the policy level， regulatory approval pathways for AI-enabled medical devices， liability attribution for AI-assisted clinical decisions， and standardized data governance frameworks still require systematic improvement. Moving forward， priority should be given to integrating explainable AI with domestically developed medical devices， establishing a standardized framework for cross-institutional data sharing and multimodal fusion， developing a clinical validation system oriented to hard clinical endpoints， and optimizing high-robustness signal quality sensing and processing technologies. We also need to facilitate cross-platform data interoperability， deepen the integration of AI tools into standard clinical workflows， and improve supporting mechanisms including health insurance reimbursement coverage， primary care capacity building， and AI-enabled closed-loop disease management. Only by establishing a full-cycle regulatory system for AI medical products， clarifying the boundaries of authority and liability for AI-assisted clinical practice， and developing unified national data governance standards can we advance the translation of AI from mere technological feasibility to universal accessible healthcare， thereby effectively reducing the overall disease burden of AF at the population level.

Key words: atrial fibrillation, artificial intelligence, electrocardiogram, deep learning, clinical applications, health equity