Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the American Academy of Ophthalmology Self-Assessment Program
Bibliographic record
Abstract
PURPOSE: 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. SETTING: Not applicable. DESIGN: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".