Evaluating Large Language Models in Ophthalmology: Systematic Review
Abstract
Background: Large language models (LLMs) have the potential to revolutionize ophthalmic care, but their evaluation practice remains fragmented. A systematic assessment is crucial to identify gaps and guide future evaluation practices and clinical integration.
Objective: This study aims to map the current landscape of LLM evaluations in ophthalmology and explore whether performance synthesis is feasible for a common task.
Methods: A comprehensive search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted up to November 17, 2024 (no language limits). Eligible publications quantitatively assessed an existing or modified LLM on ophthalmology-related tasks. Studies without full-text availability or those focusing solely on vision-only models were excluded. Two reviewers screened studies and extracted data across 6 dimensions (evaluated LLM, data modality, ophthalmic subspecialty, medical task, evaluation dimension, and clinical alignment), and disagreements were resolved by a third reviewer. Descriptive statistics were analyzed and visualized using Python (with NumPy, Pandas, SciPy, and Matplotlib libraries). The Fisher exact test compared open- versus closed-source models. An exploratory random-effects meta-analysis (logit transformation; DerSimonian-Laird τ2) was performed for the diagnosis-making task; heterogeneity was reported with I2 and subgrouped by model, modality, and subspecialty.
Results: Of the 817 identified records, 187 studies met the inclusion…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 176 participants or records.
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