TY - JOUR
T1 - Artificial Intelligence-enabled ECG
T2 - Physiologic and Pathophysiologic Insights and Implications
AU - Kashou, Anthony H.
AU - Adedinsewo, Demilade A.
AU - Siontis, Konstantinos C.
AU - Noseworthy, Peter A.
N1 - Funding Information:
Funding: The authors also acknowledge support by NIH T32 HL007111.
Publisher Copyright:
© American Physiological Society.
PY - 2022/7
Y1 - 2022/7
N2 - Advancements in machine learning and computing methods have given new life and great excite-ment 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.
AB - Advancements in machine learning and computing methods have given new life and great excite-ment 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.
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U2 - 10.1002/cphy.c210001
DO - 10.1002/cphy.c210001
M3 - Article
C2 - 35766831
AN - SCOPUS:85133215089
SN - 2040-4603
VL - 12
SP - 3417
EP - 3424
JO - Comprehensive Physiology
JF - Comprehensive Physiology
IS - 3
ER -