ChatGPT-4o and OpenAI-o1: A Comparative Analysis of Its Accuracy in Refractive Surgery
Bibliographic record
Abstract
Background: To assess the accuracy of ChatGPT-4o and OpenAI-o1 in answering refractive surgery questions from the AAO BCSC Self-Assessment Program and to evaluate whether their performance could meaningfully support clinical decision making, we compared the models with 1983 ophthalmology residents and clinicians. Methods: A randomized, questionnaire-based study was conducted with 228 text-only questions from the Refractive Surgery section of the BCSC Self-Assessment Program. Each model received the prompt, “Please provide an answer to the following questions.” Accuracy was measured as the proportion of correct answers and reported with 95 percent confidence intervals. Differences between groups were assessed with the chi-squared test for independence and pairwise comparisons. Results: OpenAI-o1 achieved the highest score (91.2%, 95% CI 87.6–95.0%), followed by ChatGPT-4o (86.4%, 95% CI 81.9–90.9%) and the average score from 1983 users of the Refractive Surgery section of the BCSC Self-Assessment Program (77%, 95% CI 75.2–78.8%). Both language models significantly outperformed human users. The five-point margin of OpenAI-o1 over ChatGPT-4o did not reach statistical significance (p = 0.1045) but could represent one additional correct decision in twenty clinically relevant scenarios. Conclusions: Both ChatGPT-4o and OpenAI-o1 significantly outperformed BCSC Program users, demonstrating a level of accuracy that could augment medical decision making. Although OpenAI-o1 scored higher than ChatGPT-4o, the difference did not reach statistical significance. These findings indicate that the “advanced reasoning” architecture of OpenAI-o1 offers only incremental gains and underscores the need for prospective studies linking LLM recommendations to concrete clinical outcomes before routine deployment in refractive-surgery practice.
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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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".