Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology

Gurukripa N. Kowlgi, Fatima M. Ezzeddine, Suraj Kapa

Research output: Contribution to journalReview articlepeer-review


Purpose of Review: Artificial intelligence (AI) is an aspect of computer technology that imitates the ability of the human mind to analyze data. Over the last few years, there has been a paradigm shift in the utilization of AI in clinical practice. It is imperative for the clinical electrophysiologist to understand the basics of AI, and its potential applications in the field as new applications are developed and implemented. Recent Findings: Multiple investigators have demonstrated various AI algorithms that can be utilized in clinical care. These include applications such as electronic stethoscopes and electrocardiographic prediction of atrial fibrillation or congestive heart failure. AI may also be used in cardiovascular imaging, to identify disease patterns and even compose preliminary reports. Summary: Herein, we seek to familiarize readers with terms associated with AI, such as machine learning and neural networks. Further, we review the applications of AI in bedside clinical calculators, electrocardiography, and the field of cardiovascular imaging. A critical appraisal of AI is provided with specific review of hurdles in the integration of AI in clinical practice.

Original languageEnglish (US)
Article number13
JournalCurrent Cardiovascular Risk Reports
Issue number9
StatePublished - Sep 1 2020


  • Artificial intelligence
  • Big data
  • Electrophysiology
  • Machine learning
  • Risk scores

ASJC Scopus subject areas

  • Pharmacology
  • Pharmacology (medical)


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