TY - JOUR
T1 - Artificial Intelligence in Cardiology
AU - Johnson, Kipp W.
AU - Torres Soto, Jessica
AU - Glicksberg, Benjamin S.
AU - Shameer, Khader
AU - Miotto, Riccardo
AU - Ali, Mohsin
AU - Ashley, Euan
AU - Dudley, Joel T.
N1 - Funding Information:
Dr. Dudley is supported by the following grants from the National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK098242; National Cancer Institute grant U54CA189201; Illuminating the Druggable Genome; Knowledge Management Center sponsored by the National Institutes of Health Common Fund; National Cancer Institute grant U54-CA189201-02; and the National Center for Advancing Translational Sciences and Clinical and Translational Science Award UL1TR000067. Dr. Shameer has received consulting fees or honoraria from McKinsey, Google, LEK Consulting, Parthenon-EY, Philips Healthcare, and Kencore Health. Dr. Dudley has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca, and Hoffman-La Roche; is a scientific advisor to LAM Therapeutics, NuMedii, and Ayasdi; and holds equity in NuMedii, Ayasdi, and Ontomics. Dr. Ashley is founder of Personalis Inc. and Deepcell Inc; and is an advisor to Genome Medical and SequenceBio. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Publisher Copyright:
© 2018 The Authors
PY - 2018/6/12
Y1 - 2018/6/12
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - cardiology
KW - machine learning
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85047558352&partnerID=8YFLogxK
U2 - 10.1016/j.jacc.2018.03.521
DO - 10.1016/j.jacc.2018.03.521
M3 - Review article
C2 - 29880128
AN - SCOPUS:85047558352
SN - 0735-1097
VL - 71
SP - 2668
EP - 2679
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
IS - 23
ER -