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This paper is in the following e-collection/theme issue:

Digital Health Reviews (1831) Generative Language Models Including ChatGPT (827) Ophthalmology (80) AI Language Models in Health Care (255) Research Methodology - AI (51)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76947, first published .
Evaluating Large Language Models in Ophthalmology: Systematic Review

Evaluating Large Language Models in Ophthalmology: Systematic Review

Evaluating Large Language Models in Ophthalmology: Systematic Review

Authors of this article:

Zili Zhang1 Author Orcid Image ;   Haiyang Zhang1 Author Orcid Image ;   Zhe Pan2 Author Orcid Image ;   Zhangqian Bi2 Author Orcid Image ;   Yao Wan2 Author Orcid Image ;   Xuefei Song1 Author Orcid Image ;   Xianqun Fan1 Author Orcid Image
Article Authors Cited by (5) Tweetations (4) Metrics

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 J, Kor A. Retrieval augmented generation (RAG) with enhanced SBERT fine-tuning vector summarization in medical domain. Expert Systems with Applications 2026:132388 View