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, Giezel Diaz-Candido, James Maningo, Victor Tseng
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.
Topics & Concepts
ConcordanceUnited States Medical Licensing ExaminationMedical educationComputer scienceLicensureMedical schoolArtificial intelligenceMedicineInternal medicineArtificial Intelligence in Healthcare and EducationHealthcare cost, quality, practicesMachine Learning in Healthcare