Evaluating Large Language Models in Ophthalmology: Systematic Review

Journals

1. Cohen L, Gupta A, Patel P, Gill G, Bains H, Gupta S. The Role of Large Language Models in Ophthalmology: A Review of Current Applications, Performance, and Future Directions. Cureus 2025 View
2. Olszewski R, Brzeziński J, Watros K, Rysz J. Quantifying Readability in Chatbot-Generated Medical Texts Using Classical Linguistic Indices: A Review. Applied Sciences 2026;16(3):1423 View
3. Cang X, Ni M, Song C, Zhao J, Guo Y, Zou Y, Zhang Z, Jiang L. ChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries. Frontiers in Cell and Developmental Biology 2026;14 View
4. Li S, Wang X, Chen Y, Tian M, Lin P, Lai M, Jiang L. Large language models for primary care ophthalmic education: a systematic review. Frontiers in Medicine 2026;13 View
5. Hariharan S, Balapriya S, Jose P, Xu Q, Lu Z, Kor A. Retrieval augmented generation (RAG) with enhanced SBERT fine-tuning vector summarization in medical domain. Expert Systems with Applications 2026;321:132388 View
6. Livieratos A, Lin J, Chasani P, Gaga M, Fousekis F, Gogos C, Akinosoglou K, Katsanos K, Gamalo M. Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges. Methods and Protocols 2026;9(3):69 View
7. Jiang L, Jiang X, Wu W, Jiang F. Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training. Frontiers in Public Health 2026;14 View
8. Nouyed M, Al-Mamun M, Adjeroh D, Hu G. Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification. Diagnostics 2026;16(10):1504 View