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How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation,
Hyunjae Kim1, Yu Yin2, Zhiyuan Cao1
1Yale University.
Research Square
|February 23, 2026
Summary
Large language models (LLMs) are transforming ophthalmology, but most studies use general models. Few LLMs are ophthalmology-specific, and clinical validation lags, hindering real-world adoption.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Informatics
Background:
- Large language models (LLMs) are increasingly applied in ophthalmology for patient care, documentation, and education.
- Existing reviews often focus narrowly, leaving the overall landscape of LLM development and application in ophthalmology undercharacterized.
- Key questions regarding LLM development, application scope, evaluation, and clinical readiness remain unanswered.
Purpose of the Study:
- To systematically review the landscape of large language models in ophthalmology.
- To categorize ophthalmic use cases and evaluate the rigor of LLM development and validation strategies.
- To identify gaps and trends in LLM research and their readiness for clinical adoption.
Main Methods:
- Conducted a systematic review of 1,029 studies from major databases (PubMed/PMC, Scopus, Embase) published between January 2022 and April 2025, identifying 91 relevant articles.
- Developed a structured framework to categorize use cases, model architectures, training data, evaluation methods, and resource availability.
- Manually annotated studies using 27 structured variables and performed illustrative evaluations of emerging LLMs.
Main Results:
- Most studies utilized general proprietary LLMs (e.g., GPT-4, Gemini); <10% featured ophthalmology-specific adaptations, and only 4% developed ophthalmology-specific architectures.
- Multimodal LLMs incorporating imaging data were underexplored (23% of studies).
- A significant translational gap exists: 57.1% used standard benchmarks, but only 9.9% performed retrospective clinical validation, and just two studies had prospective pilot evaluations.
- Reproducibility and transparency are limited, with only 5.5% releasing code and 33% using public datasets.
Conclusions:
- The current development of LLMs in ophthalmology heavily relies on general models with limited domain-specific adaptations and insufficient clinical validation.
- There is a critical need for more ophthalmology-specific LLM development, multimodal integration, and rigorous validation using real-world clinical data.
- Addressing gaps in reproducibility, transparency, and clinical validation is essential for the responsible adoption of LLMs in ophthalmic practice.
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