Nasser Ameen, Michalczak Mateusz, Żądło Anna, Tokarek Tomasz
Center for Innovative Medical Education, Jagiellonian University Medical College, 30-688 Krakow, Poland.
Center for Invasive Cardiology, Electrotherapy and Angiology, 33-300 Nowy Sacz, Poland.
J Clin Med. 2025 Jul 11;14(14):4924. doi: 10.3390/jcm14144924.
Atrial fibrillation (AF) is a common cardiac arrhythmia linked to an increased risk of stroke, heart failure, and mortality, yet its diagnosis remains challenging due to its intermittent and often asymptomatic nature. Traditional methods, such as standard electrocardiography (ECG) and prolonged cardiac monitoring, have limitations in terms of cost, accessibility, and diagnostic yield. Artificial intelligence (AI), particularly machine learning (ML) and deep learning, has emerged as a promising tool for AF detection and prediction by analyzing ECG data with high accuracy. AI models can identify subtle patterns in ECG signals that may indicate AF, even when the arrhythmia is not actively present, improving early diagnosis and risk stratification. Additionally, AI-powered ECG analysis has been integrated into wearable and mobile health devices, expanding screening capabilities beyond clinical settings. While studies have demonstrated AI's effectiveness, challenges such as data bias, model reliability across diverse populations, and regulatory considerations must be addressed before widespread clinical adoption. If these obstacles are overcome, AI has the potential to revolutionize AF management by enabling earlier detection, reducing the need for resource-intensive monitoring, and improving patient outcomes.
心房颤动（AF）是一种常见的心律失常，与中风、心力衰竭和死亡风险增加有关，然而，由于其间歇性且通常无症状的特性，其诊断仍然具有挑战性。传统方法，如标准心电图（ECG）和长时间心脏监测，在成本、可及性和诊断率方面存在局限性。人工智能（AI），特别是机器学习（ML）和深度学习，通过高精度分析心电图数据，已成为一种有前景的房颤检测和预测工具。即使心律失常未主动出现，AI模型也能识别心电图信号中可能表明房颤的细微模式，从而改善早期诊断和风险分层。此外，基于AI的心电图分析已集成到可穿戴和移动健康设备中，将筛查能力扩展到临床环境之外。虽然研究已证明AI的有效性，但在广泛临床应用之前，必须解决数据偏差、不同人群中模型的可靠性以及监管等挑战。如果克服这些障碍，AI有可能通过实现早期检测、减少对资源密集型监测的需求以及改善患者预后，彻底改变房颤的管理方式。
