Performance of ChatGPT and GPT-4 on Neurosurgery Written Board Examinations

Abstract

Abstract Background Interest surrounding generative large language models (LLMs) has rapidly grown. While ChatGPT (GPT-3.5), a general LLM, has shown near-passing performance on medical student board examinations, the performance of ChatGPT or its successor GPT-4 on specialized exams and the factors affecting accuracy remain unclear. Objective To assess the performance of ChatGPT and GPT-4 on a 500-question mock neurosurgical written boards examination. Methods The Self-Assessment Neurosurgery Exams (SANS) American Board of Neurological Surgery (ABNS) Self-Assessment Exam 1 was used to evaluate ChatGPT and GPT-4. Questions were in single best answer, multiple-choice format. Chi-squared, Fisher’s exact, and univariable logistic regression tests were employed to assess performance differences in relation to question characteristics. Results ChatGPT (GPT-3.5) and GPT-4 achieved scores of 73.4% (95% confidence interval [CI]: 69.3-77.2%) and 83.4% (95% CI: 79.8-86.5%), respectively, relative to the user average of 73.7% (95% CI: 69.6-77.5%). Question bank users and both LLMs exceeded last year’s passing threshold of 69%. While scores between ChatGPT and question bank users were equivalent ( P =0.963), GPT-4 outperformed both (both P <0.001). GPT-4 answered every question answered correctly by ChatGPT and 37.6% (50/133) of remaining incorrect questions correctly. Among twelve question categories, GPT-4 significantly outperformed users in each but performed comparably to ChatGPT in three (Functional, Other General, and Spine) and outperformed both users and ChatGPT for Tumor questions. Increased word count (odds ratio [OR]=0.89 of answering a question correctly per +10 words) and higher-order problem-solving (OR=0.40, P =0.009) were associated with lower accuracy for ChatGPT, but not for GPT-4 (both P >0.005). Multimodal input was not available at the time of this study so, on questions with image content, ChatGPT and GPT-4 answered 49.5% and 56.8% of questions correctly based upon contextual context clues alone. Conclusion LLMs achieved passing scores on a mock 500-question neurosurgical written board examination, with GPT-4 significantly outperforming ChatGPT.

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