Liu Xichong, Bandyopadhyay Sabyasachi, Rogers Albert J
Department of Cardiology, Cardiovascular Institute, Stanford University School of Medicine, Stanford, USA.
J Clin Exp Cardiolog. 2025;16(2). Epub 2025 Mar 24.
As Artificial Intelligence (AI) plays an increasingly prominent role in society, its application in clinical cardiology is gaining traction by providing innovative diagnostic, prognostic, and therapeutic solutions. Electrocardiogram (ECG), as a ubiquitous diagnostic tool in cardiology, has emerged as the leading data source for Deep Learning (DL) applications. A recent study from our group used ECG-based DL model to identify cardiac wall motion abnormalities and outperformed expert human interpretation. Motivated by this work and that of many others, we aim to discuss advances, limitations, future directions, and equity considerations in DL models for ECG-based AI applications.
随着人工智能（AI）在社会中发挥着越来越突出的作用，其在临床心脏病学中的应用通过提供创新的诊断、预后和治疗解决方案而越来越受到关注。心电图（ECG）作为心脏病学中一种普遍使用的诊断工具，已成为深度学习（DL）应用的主要数据源。我们团队最近的一项研究使用基于心电图的深度学习模型来识别心脏壁运动异常，其表现优于专家的人工解读。受这项工作以及其他许多研究的启发，我们旨在讨论基于心电图的人工智能应用中深度学习模型的进展、局限性、未来方向和平等性考量。
