Cited by 1,202Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareMachine Learning in Healthcare

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Artificial Intelligence in Cardiology

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Johnson, Kipp W., Torres Soto, Jessica, Glicksberg, Benjamin S., Shameer, Khader, Miotto, Riccardo, Ali, Mohsin, Ashley, Euan, Dudley, Joel T.·2018·Journal of the American College of Cardiology

DOI 10.1016/j.jacc.2018.03.521

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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.

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Authors: Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T.
Affiliations: Mount Sinai Health System (US)
Northwell Health (US)
Stanford Medicine (US)
Corresponding author: Dudley, Joel T.
ORCID: Johnson, Kipp W.
Glicksberg, Benjamin S.
Shameer, Khader
Miotto, Riccardo
Ali, Mohsin
Ashley, Euan
Dudley, Joel T.
Venue: Journal of the American College of Cardiology
Publisher: Elsevier BV
Published: 2018-06
Volume: 71
Issue: 23
Pages: 2668-2679
ISSN: 0735-1097 (ISSN-L)
DOI: 10.1016/j.jacc.2018.03.521
OpenAlex: W2807593075
Cited by: 1,202
References: 50
Citations by year: 2026: 134 · 2025: 176 · 2024: 180 · 2023: 147 · 2022: 198 · 2021: 156 · 2020: 111 · 2019: 83 · 2018: 16
FWCI: 2.03
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Journal of the American College of Cardiology (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: AstraZeneca
GlaxoSmithKline
F. Hoffmann-La Roche
LAM Therapeutics
National Institutes of Health (UL1TR000067)
National Cancer Institute (U54-CA189201-02, U54CA189201)
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098242)
National Center for Advancing Translational Sciences
Janssen Pharmaceuticals
Common Fund
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: artificial intelligence in cardiology, machine learning, cardiology, predictive modeling, feature selection, dichotomization, supervised learning algorithms, deep learning, unsupervised learning, precision cardiology, clinical applications of AI
MeSH terms: Machine Learning, Algorithms, Artificial Intelligence, Cardiology, Cardiovascular Diseases, Humans

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