Abstract
INTRODUCTION: Large language models (LLMs) have transformed various domains in medicine, aiding in complex tasks and clinical decision-making, with OpenAI's GPT-4, GPT-3.5, Google's Bard, and Anthropic's Claude among the most widely used. While GPT-4 has demonstrated superior performance in some studies, comprehensive comparisons among these models remain limited. Recognizing the significance of the National Board of Medical Examiners (NBME) exams in assessing the clinical knowledge of medical students, this study aims to compare the accuracy of popular LLMs on NBME clinical subject exam sample questions. METHODS: The questions used in this study were multiple-choice questions obtained from the official NBME website and are publicly available. Questions from the NBME subject exams in medicine, pediatrics, obstetrics and gynecology, clinical neurology, ambulatory care, family medicine, psychiatry, and surgery were used to query each LLM. The responses from GPT-4, GPT-3.5, Claude, and Bard were collected in October 2023. The response by each LLM was compared to the answer provided by the NBME and checked for accuracy. Statistical analysis was performed using one-way analysis of variance (ANOVA). RESULTS: A total of 163 questions were queried by each LLM. GPT-4 scored 163/163 (100%), GPT-3.5 scored 134/163 (82.2%), Bard scored 123/163 (75.5%), and Claude scored 138/163 (84.7%). The total performance of GPT-4 was statistically superior to that of GPT-3.5, Claude, and Bard by 17.8%, 15.3%, and 24.5%, respectively. The total performance of GPT-3.5, Claude, and Bard was not significantly different. GPT-4 significantly outperformed Bard in specific subjects, including medicine, pediatrics, family medicine, and ambulatory care, and GPT-3.5 in ambulatory care and family medicine. Across all LLMs, the surgery exam had the highest average score (18.25/20), while the family medicine exam had the lowest average score (3.75/5). Conclusion: GPT-4's superior performance on NBME clinical subject exam sample questions underscores its potential in medical education and practice. While LLMs exhibit promise, discernment in their application is crucial, considering occasional inaccuracies. As technological advancements continue, regular reassessments and refinements are imperative to maintain their reliability and relevance in medicine.