The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow
Open accessAbstract
IMPORTANCE: Large language model (LLM) artificial intelligence (AI) chatbots direct the power of large training datasets towards successive, related tasks, as opposed to single-ask tasks, for which AI already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as virtual physicians, has not yet been evaluated. OBJECTIVE: To evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes. DESIGN: We inputted all 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared accuracy on differential diagnoses, diagnostic testing, final diagnosis, and management based on patient age, gender, and case acuity. SETTING: ChatGPT, a publicly available LLM. PARTICIPANTS: Clinical vignettes featured hypothetical patients with a variety of age and gender identities, and a range of Emergency Severity Indices (ESIs) based on initial clinical presentation. EXPOSURES: MSD Clinical Manual vignettes. MAIN OUTCOMES AND MEASURES: We measured the proportion of correct responses to the questions posed within the clinical vignettes tested. RESULTS: ChatGPT achieved 71.7% (95% CI, 69.3% to 74.1%) accuracy overall across all 36 clinical vignettes. The LLM demonstrated the highest performance in making a final diagnosis with an accuracy of 76.9% (95% CI, 67.8% to 86.1%), and the lowest performance in generating an initial differential diagnosis with an accuracy of 60.3% (95% CI, 54.2% to 66.6%). Compared to answering questions about general medical knowledge, ChatGPT demonstrated inferior performance on differential diagnosis (β=-15.8%, p<0.001) and clinical management (β=-7.4%, p=0.02) type questions. CONCLUSIONS AND RELEVANCE: ChatGPT achieves impressive accuracy in clinical decision making, with particular strengths emerging as it has more clinical information at its disposal.
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“Overall, the use of AI chatbots in nursing education faces multifaceted challenges, including technological limitations, ethical considerations, and social acceptance (59).”
From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [59]
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Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Kung, T. H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., et al. (2022) · doi:10.1101/2022.12.19.22283643 · Cited by 4,115
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Unequal Treatment—Confronting Racial and Ethnic Disparities in Healthcare
Osundeko, O. (2004) · PubMed Central · Cited by 797
Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021) · JMIR Cancer · doi:10.2196/27850 · Cited by 571
ChatGPT Goes to Law SchoolPreprint
Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4335905 · Cited by 565
Artificial intelligence and algorithmic bias: implications for health systems
Panch, T., Mattie, H., & Atun, R. (2019) · Journal of Global Health · doi:10.7189/jogh.09.020318 · Cited by 533
Flanagin, A., Bibbins-Domingo, K., Berkwits, M., & Christiansen, S. L. (2023) · JAMA · doi:10.1001/jama.2023.1344 · Cited by 420
Research works printed but not linked: 1
- Case studies. Merck Manuals Professional Edition. Accessed February 1, 2023. https://www.merckmanuals.com/professional/pages-with-widgets/case-studies?mode=list
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Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
Utility of ChatGPT in Clinical Practice
Liu, J., Wang, C., & Liu, S. (2023) · Journal of Medical Internet Research · doi:10.2196/48568 · Cited by 505
“• Clinical decision-making: Rao et al [6] • Cancer screening: Rao et al [7] compared ChatGPT responses with the American College of Radiology appropriateness criteria for breast pain and breast cancer screening.”
Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, K., & Succi, M. D. (2023) · Journal of Medical Internet Research · doi:10.2196/48659 · Cited by 412
“Recently, there has been great interest in using the nascent but powerful chatbot for clinical decision support [16-20].”
Self-citationHaltaufderheide, J., & Ranisch, R. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01157-x · Cited by 396
“Present-day LLMs, such as ChatGPT, are considered to have a promising accuracy in clinical decision-making, 11,12 diagnosis, 13 symptom-assessment, and triage-advise.”
BackgroundLarge language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Metadata
- Authors
- Rao, A., Pang, M., Kim, J., Kamineni, M., Lie, W., Prasad, A. K., Landman, A., Dreyer, K. J., & Succi, M. D.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Succi, Marc D.
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2023-02-26
- PMC
- PMC9980239
- OpenAlex
- W4322208207
- Cited by
- 134
- References
- 24
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 55 · 2023: 50
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (T32GM144273)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- ChatGPT, large language models, clinical decision support, differential diagnosis, clinical vignettes, emergency medicine, clinical reasoning, diagnostic accuracy, clinical management, Emergency Severity Index
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