- Published
- 2023-02-09
- Venue
- PLOS Digital Health, vol. 2, no. 2, pp. e0000198
- Cited by
- 4,092
- Open access
- Yes, gold
- DOI
- 10.1371/journal.pdig.0000198
- PubMed
- 36812645
Abstract
We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.
Cite this paper
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- APA 7
Kung, T. H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., Leon, L. D., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198
- MLA 9
Kung, Tiffany H., et al. "Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models." PLOS Digital Health, vol. 2, no. 2, 2023, pp. e0000198, https://doi.org/10.1371/journal.pdig.0000198.
- Chicago
Kung, Tiffany H., Morgan Cheatham, ChatGPT, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, et al. "Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models." PLOS Digital Health 2, no. 2 (2023): e0000198. https://doi.org/10.1371/journal.pdig.0000198.
- GOST
Kung T. H., Cheatham M., ChatGPT [и др.] Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models // PLOS Digital Health. — 2023. — Vol. 2, № 2. — P. e0000198. — URL: https://doi.org/10.1371/journal.pdig.0000198
- BibTeX
@article{kung2023performance, title = {{Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models}}, author = {Tiffany H. Kung and Morgan Cheatham and ChatGPT and Arielle Medenilla and Czarina Sillos and Lorie De Leon and Camille Elepaño and Maria Madriaga and Rimel Aggabao and Giezel Diaz-Candido and James Maningo and Victor Tseng}, journal = {PLOS Digital Health}, year = {2023}, volume = {2}, number = {2}, pages = {e0000198}, doi = {10.1371/journal.pdig.0000198}, url = {https://doi.org/10.1371/journal.pdig.0000198} }- RIS
TY - JOUR AU - Kung, Tiffany H. AU - Cheatham, Morgan AU - ChatGPT AU - Medenilla, Arielle AU - Sillos, Czarina AU - Leon, Lorie De AU - Elepaño, Camille AU - Madriaga, Maria AU - Aggabao, Rimel AU - Diaz-Candido, Giezel AU - Maningo, James AU - Tseng, Victor TI - Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models JO - PLOS Digital Health VL - 2 IS - 2 SP - e0000198 PY - 2023 DA - 2023/02/09 DO - 10.1371/journal.pdig.0000198 UR - https://doi.org/10.1371/journal.pdig.0000198 ER -
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