An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'?

Journal: Cardiovascular digital health journal
Published Date: May 5, 2021

Abstract

OBJECTIVE: To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm capable of comprehensive, human-like ECG interpretation and compare its diagnostic performance against conventional ECG interpretation methods.

Authors

  • Anthony H Kashou
    Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Siva K Mulpuru
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Abhishek J Deshmukh
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Wei-Yin Ko
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

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