Artificial intelligence in cardiology
Open accessAbstract
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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AI tools have the potential to outperform experts in detecting and predicting cardiovascular disease.
“Moreover, AI will soon revolutionize cardiovascular health, as its tools have the potential to outperform experts in detecting and predicting cardiovascular disease [10,11,12].”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [11]
Both machine learning and deep learning need enormous data sets to enable complex but accurate algorithms.
“First, both machine learning and deep learning need enormous data sets to enable complex but accurate algorithms [48].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [48]
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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
- 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)
- 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
- 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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