Karakasis Paschalis, Theofilis Panagiotis, Sagris Marios, Pamporis Konstantinos, Stachteas Panagiotis, Sidiropoulos Georgios, Vlachakis Panayotis K, Patoulias Dimitrios, Antoniadis Antonios P, Fragakis Nikolaos
Second Department of Cardiology, Hippokration General Hospital, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece.
First Cardiology Department, School of Medicine, Hippokration General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
J Clin Med. 2025 Apr 11;14(8):2627. doi: 10.3390/jcm14082627.
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.
心房颤动（AF）是最常见的心律失常，与显著的发病率、死亡率和医疗负担相关。尽管房颤管理取得了进展，但在早期检测、风险分层和治疗优化方面仍存在挑战，因此需要创新解决方案。人工智能（AI）已成为房颤护理中的变革性工具，利用机器学习和深度学习算法提高诊断准确性、改善风险预测并指导治疗干预。人工智能驱动的心电图筛查已证明能够检测无症状房颤，而基于可穿戴光电容积脉搏波描记术的技术已将实时心律监测扩展到临床环境之外。人工智能驱动的预测模型整合电子健康记录和多模态生理数据，以完善房颤风险分层、中风预测和抗凝决策。在治疗领域，人工智能正在彻底改变个体化治疗，并优化抗凝管理和导管消融策略。值得注意的是，人工智能增强的电解剖标测和实时手术指导有望提高消融成功率并减少房颤复发。尽管取得了这些进展，但人工智能在房颤管理中的临床整合仍是一个不断发展的领域。未来的研究应侧重于大规模验证、模型可解释性和监管框架，以确保广泛应用。本综述探讨了人工智能在房颤中的当前和新兴应用，强调了其增强精准医学和改善患者预后的潜力。
