Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association
Open accessAbstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Currently AI-based tools have yet to demonstrate improved patient outcomes at scale.
“Currently AI-based tools have yet to demonstrate improved patient outcomes at scale.11”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [11]
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Wu, J. T.-Y., de la Hoz, M. Á. A., Kuo, P., Paguio, J. A., Yao, J. S., Dee, E. C., Yeung, W. L. K., Jurado, J., Moulick, A. N., Milazzo, C., et al. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00674-z · Cited by 13 · see citing works
Self-citationEmerging developments in citizen science: Reflecting on areas of innovation
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200,000 whole genomes made available for biomedical studies
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Diagnosing atrial fibrillation by mobile technology: physician decision or device provision?
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Künstliche Intelligenz und Radiomics
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Flagged first, then by citations
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Cited by
Sorted by citations
Artificial intelligence in nursing: an integrative review of clinical and operational impacts
Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134 · see citing works
Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine
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Implications of integrating large language models into clinical decision making
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Self-citationAcceptance of Technological Innovations in Emergency Departments
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- Authors
- Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., Lett, E., Menon, B. K., Sheehan, K. A., Al-Zaiti, S. S., & on behalf of the American Heart Association Institute for Precision Cardiovascular Medicine; Council on Cardiovascular and Stroke Nursing; Council on Lifelong Congenital Heart Disease and Heart Health in the Young; Council on Cardiovascular Radiology and Intervention; Council on Hypertension; Council on the Kidney in Cardiovascular Disease; and Stroke Council
- ORCID
- Venue
- Circulation
- Published
- 2024-04-02
- Volume
- 149
- Issue
- 14
- Pages
- e1028-e1050
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC11042786
- OpenAlex
- W4392238077
- Cited by
- 347
- References
- 181
- Citations by year
- 2026: 181 · 2025: 137 · 2024: 28 · 2022: 1
- FWCI
- 7.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL083359, R01HL149134, R01HL162260, R01HL064541)
- 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, cardiovascular disease, stroke, precision medicine, diagnosis, disease classification, treatment, digital health tools, algorithmic bias, clinical teaching, implementation science, cardiovascular outcomes
- MeSH terms
- American Heart Association, Artificial Intelligence, Cardiovascular Diseases, Heart Diseases, Humans, United States, Stroke
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 ›
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