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J Am Coll Cardiol. 2018 Jun 12;71(23):2668-2679. doi: 10.1016/j.jacc.2018.03.521.

Artificial Intelligence in Cardiology.

Author information

1
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.
2
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.
3
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; Institute for Computational Health Sciences, University of California, San Francisco, California.
4
Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, New York.
5
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. Electronic address: joel.dudley@mssm.edu.

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.

KEYWORDS:

artificial intelligence; cardiology; machine learning; precision medicine

PMID:
29880128
DOI:
10.1016/j.jacc.2018.03.521
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