Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the...

To assess the accuracy of leading large language models (LLMs) in answering cataract and refractive surgery questions, determine whether prompt complexity affects performance, and compare their accuracy with ophthalmology residents.

Not applicable.

Randomized questionnaire-based study using 100 questions from the cataract and refractive surgery section of the Basic and Clinical Science Course (BCSC) Self-Assessment Program.

5 LLMs (ChatGPT-4, ChatGPT-4o, Gemini, Gemini Advanced, and Copilot-Precise Mode) were tested. Data from 1983 BCSC Self-Assessment Program users served as a comparison. Each LLM underwent 2 sessions: one with a simple prompt and another with a contextualized prompt. Accuracy was defined as the proportion of correct answers.

Using the simple prompt, ChatGPT-4o achieved the highest accuracy at 84% (95% CI 77%-91%), followed by Gemini Advanced at 82%, Copilot at 78%, ChatGPT-4 at 77%, and Gemini at 62% ( P < .05). With the contextualized complex prompt, ChatGPT-4o again led (86%; 95% CI 79%-93%). Performance differences between simple and complex prompts were not statistically significant ( P > .05). Except for Gemini, all models' lower 95% CIs exceeded the 60% passing threshold. The mean resident score was 77% (95% CI 75%-79%). Only ChatGPT-4o significantly ( P = .04) outperformed residents, while Gemini Advanced and Copilot trended higher ( P ∼ .10).

ChatGPT-4o consistently outperformed ChatGPT-4, Gemini, Gemini Advanced, and Copilot, and was the only model to significantly surpass residents. Prompt complexity did not affect LLM performance. All models except Gemini exceeded the 60% accuracy threshold, indicating the potential of LLMs as tools for knowledge assessment in refractive surgery.