Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype

Adam J. Russak, Farhan Chaudhry, Jessica K. De Freitas, Garrett Baron, Fayzan F. Chaudhry, Solomon Bienstock, Ishan Paranjpe, Akhil Vaid, Mohsin Ali, Shan Zhao, Sulaiman Somani, Felix Richter, Tejeshwar Bawa, Phillip D. Levy, Riccardo Miotto, Girish N. Nadkarni, Kipp W. Johnson, Benjamin S. Glicksberg

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations


Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning’s ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML’s growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Original languageEnglish
Pages (from-to)379-390
Number of pages12
JournalJournal of Cardiovascular Pharmacology and Therapeutics
Issue number5
StatePublished - 1 Sep 2020


  • artificial intelligence
  • cardiology
  • cardiovascular disease
  • deep learning
  • machine learning


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