The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Self-citationReal-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Serkan Kiranyaz, B., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,997
Predicting good probabilities with supervised learning
Niculescu-Mizil, A., & Caruana, R. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102430 · Cited by 1,884
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Self-citation
Research works printed but not linked: 1
- Harrell FE. How To Do Bad Biomarker Research. Vanderbilt Center For Quantitative Sciences Workshop, Nashville, Tennessee, 2015.
Cited by
Sorted by citations
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020) · Business Process Management Journal · doi:10.1108/bpmj-10-2019-0411 · Cited by 1,280
Privacy and artificial intelligence: challenges for protecting health information in a new era
Murdoch, B. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00687-3 · Cited by 991
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511
The Recent Progress and Applications of Digital Technologies in Healthcare: A Review
Senbekov, M., Saliev, T. M., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Джамбуловна, А. Н., Toishibekov, Y., & Fakhradiyev, I. R. (2020) · International Journal of Telemedicine and Applications · doi:10.1155/2020/8830200 · Cited by 513
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
