Comparing Performance of Large Language Model-Based Tools on Patient-Driven Glaucoma Inquiries
Bibliographic record
Abstract
PRÉCIS: GPT-4o and GPT-4o Mini outperformed Gemini Pro in effectively answering glaucoma-related questions, suggesting that GPT models provide high-quality information and highlights the potential of AI chatbots to deliver medically relevant knowledge. PURPOSE: Large language models (LLMs) can assist patients who seek medical knowledge online to guide their own glaucoma care. Understanding the differences in LLM performance on glaucoma-related questions can inform patients about the best resources to obtain relevant information. METHODS: This cross-sectional study evaluated the accuracy, comprehensiveness, quality, and readability of LLM-generated responses to glaucoma inquiries. Seven questions posted by patients on the American Academy of Ophthalmology's Eye Care Forum were randomly selected and prompted into GPT-4o, GPT-4o Mini, Gemini Pro, and Gemini Flash in September 2024. Four physicians practicing ophthalmology assessed responses using a Likert scale based on accuracy, comprehensiveness, and quality. The Flesch-Kincaid Grade level measured readability, while Bidirectional Encoder Representations from Transformers (BERT) Scores measured semantic similarity between LLM responses. Statistical analysis involved either the Kruskal-Wallis test with Dunn post-hoc test or ANOVA analysis with Tukey Honestly Significant Difference (HSD) test. RESULTS: GPT-4o rated higher in accuracy ( P =0.016), comprehensiveness ( P =0.007), and quality ( P =0.002) compared with Gemini Pro. GPT-4o Mini rated higher in comprehensiveness ( P =0.011) and quality ( P =0.007). Gemini Flash and Gemini Pro were similar across all criteria. There were no differences in readability, and LLMs mostly produced semantically similar responses. CONCLUSIONS: GPT models surpass Gemini Pro in addressing commonly asked questions about glaucoma, providing valuable insights into the application of LLMs for providing health information.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".