Artificial Intelligence-enabled ECG: Physiologic and Pathophysiologic Insights and Implications

Anthony H. Kashou, Demilade A. Adedinsewo, Konstantinos C. Siontis, Peter A. Noseworthy

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)3417-3424
Number of pages8
JournalComprehensive Physiology
Issue number3
StatePublished - Jul 2022

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)


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