Large language models for ophthalmic examination understanding: from information extraction to clinical decision support

Abstract

Ophthalmology relies on heterogeneous examination outputs, including device-generated reports, structured measurements, fundus photographs, optical coherence tomography (OCT), visual field plots, corneal imaging, and multimodal clinical data. Publicly available large language models (LLMs) and multimodal large language models (MLLMs) can process these inputs without study-specific ophthalmic training, creating opportunities for information extraction, interpretation, and clinical decision support. This Mini Review synthesizes 37 peer-reviewed studies evaluating such models across ophthalmic examination types and task layers. The evidence shows a consistent task gradient. Structured extraction and constrained calculations are generally more reliable than open-ended image interpretation, multimodal diagnosis, or treatment planning. Performance improves when inputs are standardized, clinical context is provided, and prompts or decision rules are constrained, but remains sensitive to report layout, image preprocessing, prompt design, and model updates. Common limitations include retrospective or public datasets, small or selected cohorts, limited external validation, possible training-data contamination, inconsistent reporting of prompts and model access, and a focus on technical accuracy rather than clinical outcomes. Clinical translation therefore requires safeguards matched to task risk: structured outputs and deterministic checks for extraction, traceable evidence and cross-device validation for interpretation, and independent verification, deferral mechanisms, and clinician oversight for decision support. Publicly available LLMs and MLLMs are evolving from report-parsing tools toward broader ophthalmic clinical assistants, but their use should follow a task-layered validation framework in which verification and human oversight increase with the clinical consequences of error.