The last decade has seen extraordinary progress in the development and refinement of artificial intelligence (AI)–enabled tools for clinical applications.^1,2 In cardiovascular medicine, deep learning algorithms using data from echocardiography and electrocardiography (ECG) have demonstrated superhuman performance in tasks typically done by physicians^1,3 and identified subtle phenotypes invisible to a clinician’s routine review.^4,5 Progress has been particularly quick in AI interpretation of ECGs, where the input data are well structured and the medical infrastructure for data acquisition and storage is relatively standardized across health systems. Already, there have been randomized clinical trials⁴ showing the effects of ECG-based AI algorithms on patient care. Compared with other forms of medical imaging, ECG waveforms routinely undergo signal processing and standardization that makes generalization across systems and devices easier. The ongoing development of AI systems for interpretation of ECGs is a promising avenue for extracting additional value from this readily available medical test.

在过去的十年中，人工智能 (AI) 支持的临床应用工具的开发和改进取得了非凡的进展。^{1 ,2}在心血管医学中，使用来自超声心动图和心电图 (ECG) 数据的深度学习算法在通常由医生^{1,3完成的任务中表现出超人的表现，}并确定了临床医生常规检查看不到的细微表型。^{4 ,5}在心电图的人工智能解释方面进展特别快，其中输入数据结构良好，用于数据采集和存储的医疗基础设施在整个卫生系统中相对标准化。已经有随机临床试验⁴展示了基于 ECG 的 AI 算法对患者护理的影响。与其他形式的医学成像相比，ECG 波形通常会进行信号处理和标准化，这使得跨系统和设备的泛化变得更加容易。用于解释心电图的人工智能系统的持续发展是从这种现成的医学测试中提取附加值的有希望的途径。