Artificial intelligence-enabled ECG models demonstrate high diagnostic yield for identifying conventional and unique electrocardiographic signatures, though the specific features detected remain unclear.
AI-enabled ECG offers tremendous clinical potential by detecting physiologic and pathophysiologic signatures that are typically missed by human interpretation.
Abstract Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.
Kashou et al. (Fri,) conducted a review in Cardiovascular disease / ECG interpretation. Artificial intelligence-enabled ECG (AI-ECG) was evaluated. Artificial intelligence-enabled ECG models demonstrate high diagnostic yield for identifying conventional and unique electrocardiographic signatures, though the specific features detected remain unclear.