Large language models for primary care ophthalmic education: a systematic review
Abstract
BACKGROUND: Primary care physicians (PCPs) are a critical first contact for eye-health screening, risk stratification, and referral, yet ophthalmology training and point-of-care support in primary care remain insufficient. Recent advances in generative artificial intelligence (generative AI), particularly large language models (LLMs), may help address these gaps through conversational, scenario-based learning and structured feedback. However, the educational effectiveness, reproducibility, and safety boundaries of LLM-enabled tools in primary care ophthalmology remain unclear.
METHODS: We conducted a systematic review of studies evaluating or applying LLMs in ophthalmic education, training, assessment, or primary care-relevant clinical support. PubMed, Web of Science Core Collection, and Scopus were searched from January 1, 2020 to December 31, 2025 using combined terms related to LLMs/generative AI, ophthalmology, and education or assessment. Citation chaining was also performed to reduce omission. Two reviewers independently screened records and extracted data.
RESULTS: The evidence base is dominated by vignette-based benchmarks, comparative scoring studies, and evaluations conducted in limited-sample or controlled settings; prospective real-world validation using learner transfer, clinical behavior change, workflow impact, or patient outcomes remains scarce. Across studies, LLMs can serve as "cognitive apprenticeship" partners by externalizing clinical reasoning and…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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