Artificial Intelligence in Cardiology.

Journal: Journal of the American College of Cardiology
Published Date: Jun 12, 2018

Abstract

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

Authors

  • Kipp W Johnson
  • Jessica Torres Soto
    Division of Cardiovascular Medicine, Stanford University, Palo Alto, California; Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Palo Alto, California; Center for Inherited Cardiovascular Disease, Stanford University, Palo Alto, California.
  • Benjamin S Glicksberg
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA.
  • Khader Shameer
    From the Zena and Michael A. Wiener Cardiovascular Institute and the Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York, NY (P.P.S., M.B.); Saffron Technology, Inc, Cary, NC (Y.-M.H., A.A., M.F., W.G.); and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY (K.S., J.T.D.).
  • Riccardo Miotto
    Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA, and also with the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA.
  • Mohsin Ali
    Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Euan Ashley
    Division of Cardiovascular Medicine, Stanford University, Palo Alto, California; Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Palo Alto, California; Center for Inherited Cardiovascular Disease, Stanford University, Palo Alto, California.
  • Joel T Dudley
    1Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA.

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