Artificial intelligence versus ophthalmology experts: Comparative analysis of responses to blepharitis patient queries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective To assess the accuracy and clinical education value of responses from AI models (GPT-3.5, GPT-4o, Gemini, Gemini Advanced) compared to expert ophthalmologists’ answers to common patient questions about blepharitis, and evaluate their potential for patient education and clinical use. Methods Thirteen frequently asked questions about blepharitis were selected. Responses were generated by AI models and compared to expert answers. A panel of ophthalmologists rated each response for correctness and clinical education value using a 7-point Likert scale. The Friedman test with post hoc comparisons was used to identify performance differences. Results Expert responses had the highest correctness (6.3) and clinical education value (6.4) scores, especially in complex, context-driven questions. Significant differences were found between expert and AI responses ( P < 0.05). Among AI models, GPT-3.5 performed best in simple definitions (correctness: 6.4) but dropped to 5.5 in nuanced cases. GPT-4o followed (5.4), while Gemini and Gemini Advanced scored lower (5.0 and 4.9), especially in diagnostic and treatment contexts. Conclusions AI models can support patient education by effectively answering basic factual questions about blepharitis. However, their limitations in complex clinical scenarios highlight the continued need for expert input. While promising as educational tools, AI should complement—not replace—clinician guidance in patient care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it