The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care
- PMID: 40540063
- DOI: 10.1007/s11886-025-02250-7
The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care
Abstract
Purpose of review: To review current artificial intelligence (AI) applications impacting cardiovascular disease care in women.
Recent findings: Women differ from men in cardiovascular anatomy, physiology, presentation, and treatment response, yet face disparities due to underrepresentation in trials and referral bias. AI applications offer promising tools to close these gaps by enhancing screening, diagnosis, monitoring, and treatment. This review explores female representation, outcomes, and future directions in AI-driven advancements in coronary artery disease, heart failure with preserved ejection fraction, valvular heart disease, ischemic and nonischemic cardiomyopathies, including peripartum cardiovascular disease. AI holds the potential to transform cardiovascular disease care in women by leveraging multidimensional datasets for sex-specific screening, risk prediction, prognostic phenomapping and therapeutic decision support. Expanding female representation and integrating sex-specific factors in AI research are essential to minimize bias, ensure robust external validation and enable equitable, scalable implementation.
Keywords: Artificial Intelligence; Deep Learning; Health Equity; Machine Learning; Sex-specific Differences; Women’s Cardiovascular Disease.
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Conflict of interest statement
Declarations. Human and Animal Rights and Informed Consent: No animal or human subjects by the authors were used in this study. Competing Interests: The authors declare no competing interests.
References
-
- Vogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, et al. The lancet women and cardiovascular disease commission: reducing the global burden by 2030. Lancet. 2021;397(10292):2385–438. https://doi.org/10.1016/S0140-6736(21)00684-X . - DOI - PubMed
-
- Jin X, Chandramouli C, Allocco B, Gong E, Lam CSP, Yan LL. Women’s participation in cardiovascular clinical trials from 2010 to 2017. Circulation. 2020;141(7):540–8. https://doi.org/10.1161/CIRCULATIONAHA.119.043594 . - DOI - PubMed
-
- Vervoort D, Wang R, Li G, Filbey L, Maduka O, Brewer LC, et al. Addressing the global burden of cardiovascular disease in women: JACC State-of-the-Art review. J Am Coll Cardiol. 2024;83(25):2690–707. https://doi.org/10.1016/j.jacc.2024.04.028 . - DOI - PubMed
-
- Sarraju A, Ouyang D. Navigating the Gray Zone: AI Decision Support to Identify Aortic Stenosis Severity. JACC Adv. vol 9. United States2024. p. 101178.
-
- Mihan A, Pandey A, Van Spall HG. Mitigating the risk of artificial intelligence bias in cardiovascular care. Lancet Digit Health. 2024;6(10):e749–54. https://doi.org/10.1016/S2589-7500(24)00155-9 . - DOI - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
Research Materials
