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Artificial Intelligence in Cardiology

Kipp W. JohnsonInstitute 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 YorkJessica Torres SotoDivision 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, CaliforniaBenjamin S. GlicksbergInstitute 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, CaliforniaKhader ShameerDepartment of Information Services, Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, New YorkRiccardo MiottoInstitute 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 YorkMohsin AliInstitute 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 YorkEuan A. AshleyDivision 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, CaliforniaJoel T. DudleyInstitute 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: [email protected]

2018en

ABI

Annotatsiya

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.

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Identifikatorlar

DOI: 10.1016/j.jacc.2018.03.521

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