Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions
Open accessAbstract
Cardiovascular diseases remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid evolution of artificial intelligence (AI), particularly with large language models such as ChatGPT, has introduced transformative possibilities in cardiovascular medicine. This review examines ChatGPT’s broad applications in enhancing clinical decision-making—covering symptom analysis, risk assessment, and differential diagnosis; advancing medical education for both healthcare professionals and patients; and supporting research and academic communication. Key challenges associated with ChatGPT, including potential inaccuracies, ethical considerations, data privacy concerns, and inherent biases, are discussed. Future directions emphasize improving training data quality, developing specialized models, refining AI technology, and establishing regulatory frameworks to enhance ChatGPT’s clinical utility and mitigate associated risks. As cardiovascular medicine embraces AI, ChatGPT stands out as a powerful tool with substantial potential to improve therapeutic outcomes, elevate care quality, and advance research innovation. Fully understanding and harnessing this potential is essential for the future of cardiovascular health.
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ChatGPT in this study achieved a relatively low false negative fraction.
“Compared to previous studies, ChatGPT in our study achieve the relatively low FNF [16, 29].”
From Artificial intelligence for the science of evidence synthesis: how good are AI-powered tools for automatic literature screening? (2025) · cites this paper as [29]
Section 5 (page not available) reports low false-negative counts and 95% sensitivity for ChatGPT in a previous abstract-screening study. It does not report the citing authors’ study or compare its false-negative fraction with that earlier result, so it cannot establish the claimed relative difference. (LLM, conf=0.83)
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O CHATGPT NA EDUCAÇÃO MÉDICA E OS OBJETIVOS DE DESENVOLVIMENTO SUSTENTÁVEL
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Buğra, A. K. (2026) · Journal of Health Sciences and Medicine · doi:10.32322/jhsm.1891905
- Authors
- Leon, M., Ruaengsri, C., Pelletier, G., Bethencourt, D., Shibata, M., Flores, M. Q., & Shudo, Y.
- Affiliations
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Drive, Falk CVRB, Stanford, CA 94305, USA
- Stanford Medicine (US)
- Corresponding author
- Shudo, Yasuhiro
- ORCID
- Venue
- Journal of Clinical Medicine
- Publisher
- MDPI AG
- Published
- 2024-10-31
- Volume
- 13
- Issue
- 21
- Pages
- 6543
- ISSN
- 2077-0383 (ISSN-L)
- PMC
- PMC11546989
- OpenAlex
- W4403978797
- Cited by
- 12
- References
- 68
- Citations by year
- 2026: 5 · 2025: 7
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Clinical Medicine (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Cardiovascular Health Innovation Fund (2024)
- 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 › Artificial Intelligence Applications
- Keywords
- ChatGPT, large language models, clinical decision support, cardiovascular risk assessment, differential diagnosis, medical education, AI-generated research, data privacy, algorithmic bias, AI regulation, training data quality
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