Performance of ChatGPT as an AI-assisted decision support tool in medicine: a proof-of-concept study for interpreting symptoms and management of common cardiac conditions (AMSTELHEART-2)
Open accessPreprint — published as 10.1080/00015385.2024.2303528
Abstract
Background: It is thought that ChatGPT, an advanced language model developed by OpenAI, may in the future serve as an AI-assisted decision support tool in medicine. Objective: To evaluate the accuracy of ChatGPT’s recommendations on medical questions related to common cardiac symptoms or conditions. Methods: We tested ChatGPT’s ability to address medical questions in two ways. First, we assessed its accuracy in correctly answering cardiovascular trivia questions (n=50), based on quizzes for medical professionals. Second, we entered 20 clinical case vignettes on the ChatGPT platform and evaluated its accuracy compared to expert opinion and clinical course. Results: We found that ChatGPT correctly answered 74% of the trivia questions, with slight variation in accuracy in the domains coronary artery disease (80%), pulmonary and venous thrombotic embolism (80%), atrial fibrillation (70%), heart failure (80%) and cardiovascular risk management (60%). In the case vignettes, ChatGPT’s response matched in 90% of the cases with the actual advice given. In more complex cases, where physicians (general practitioners) asked other physicians (cardiologists) for assistance or decision support, ChatGPT was correct in 50% of cases, and often provided incomplete or inappropriate recommendations when compared with expert consultation. Conclusions: Our study suggests that ChatGPT has potential as an AI-assisted decision support tool in medicine, particularly for straightforward, low-complex medical questions, but further research is needed to fully evaluate its potential.
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The economic model can have negative downstream effects on accessibility and affordability.
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From The ethics of ChatGPT in medicine and healthcare: a systematic review on large language models (LLMs) (2024) · cites this paper as [43]
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Barbaric, A., Benseler, S. M., Lalloo, C., Liu, X. C., Mariakakis, A. T., Phạm, Q., Vastert, S. J., Yeung, R. S. M., Cafazzo, J. A., & Swart, J. F. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001655
- Authors
- Harskamp, R. E., & De Clercq, L.
- Affiliations
- Amsterdam UMC Location University of Amsterdam (NL)
- Corresponding author
- Harskamp, Ralf E.
- Venue
- Acta cardiologica. Supplementum
- Publisher
- openRxiv
- Published
- 2023-03-26
- Volume
- 79
- Issue
- 3
- Pages
- 358-366
- ISSN
- 0373-7934 (ISSN-L)
- OpenAlex
- W4391779256
- Cited by
- 92
- References
- 7
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 35 · 2023: 17
- FWCI
- 7.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Acta cardiologica. Supplementum (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- 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, AI-assisted decision support, cardiovascular symptoms, cardiac conditions, clinical case vignettes, model accuracy, coronary artery disease, atrial fibrillation, heart failure, thromboembolism, cardiovascular risk management
- MeSH terms
- Proof of Concept Study, Artificial Intelligence, Cardiovascular Diseases, Decision Support Techniques, Heart Diseases, Humans, Decision Support Systems, Clinical
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