Seeing Beyond Borders: Evaluating LLMs in Multilingual Ophthalmological Question Answering
Bibliographic record
Abstract
Large Language Models (LLMs), such as GPT-3.5 [1] and GPT-4 [2], have significant potential for transforming several aspects of patient care from clinical note summarization to performing board-level clinical question-answering tasks [3], [4]. Ophthalmology, is a field with high patient volume and therefore holds high documentation burden for physicians but great opportunities for leveraging LLMs. Furthermore, given the critical and permanent nature of negative disease outcomes like blindness and their ensuing social and financial damage to patients, the need for reliable, accessible, and robust tools is urgent. Several studies have already showcased the practicality of GPT applications in ophthalmology [5], [6], and in specific ophthalmology subspecialties, such as glaucoma and retina [7], [8]
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".