Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao 等 11 位
Massachusetts General Hospital Brown University Warren Alpert Foundation
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摘要与影响
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.
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生物医学Artificial Intelligence in Healthcare and Education
Healthcare cost, quality, practices · Machine Learning in Healthcare
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