Performance of Large Language Models and Top-Decile Doctors on an Undergraduate Ophthalmology Examination

The rapid advancements of artificial intelligence (AI) and large language models (LLMs) have led to their increasing use in medical education and clinical practice. While studies demonstrate their proficiency in passing medical examinations, it remains unclear how their performance compares to that of human experts on a challenging, domain-specific examination.

This study compared the performance of four contemporary LLMs (ChatGPT-5 (OpenAI, San Francisco, CA), Claude Sonnet 4 (Anthropic, San Francisco, CA), Gemini Pro (Google, Mountain View, CA), and Perplexity (Perplexity AI, San Francisco, CA)) with a cohort of three doctors who had all recently scored in the top 10% of candidates on the Duke Elder Undergraduate Prize Examination. Participants were assessed on a 100 multiple-choice question (MCQ) examination that mimicked the style and difficulty of the prize exam. A two-tailed independent samples t-test was used for a primary comparison of the pooled LLM and pooled doctor cohorts, while a one-way ANOVA with Tukey's HSD post-hoc analysis was used for a secondary comparison of the four individual LLMs and the pooled doctor cohort.

The pooled LLM cohort, with a mean score of 85.50%, performed similarly to the pooled doctor cohort, which scored 75.67%. A primary analysis revealed no statistically significant difference between these groups (p = 0.0533). However, a secondary analysis found a significant difference among the individual LLMs and pooled doctor cohorts (p = 0.0370), with a post-hoc analysis showing that Claude Sonnet 4 (91.00%) significantly outperformed the pooled doctors (p = 0.0268). A sub-analysis on the optics subsection of the exam showed no significant difference between the pooled LLM cohort and the pooled doctor cohort (p = 0.5072, and no significant difference between individual LLMs and the pooled doctor cohort (p = 0.3545, respectively).

The findings suggest that popular LLMs, particularly Claude Sonnet 4, have achieved a level of performance on par with high-performing doctors for a challenging ophthalmology examination. This study highlights the immense potential of LLMs as valuable educational tools in ophthalmology, while also underscoring the importance of their continued evaluation against rigorous clinical benchmarks and human oversight in practice.