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

Generative Language Models Including ChatGPT (708) Chatbots and Conversational Agents (740) Ophthalmology (67) Artificial Intelligence (AI) in Medical Education (182) Clinical Information and Decision Making (1915)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60226, first published .
Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study

Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study

Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study

Authors of this article:

Shuai Ming1, 2, 3 Author Orcid Image ;   Xi Yao1 Author Orcid Image ;   Xiaohong Guo1 Author Orcid Image ;   Qingge Guo1, 2, 3 Author Orcid Image ;   Kunpeng Xie4 Author Orcid Image ;   Dandan Chen1, 2, 3 Author Orcid Image ;   Bo Lei1, 2, 3 Author Orcid Image
Article Authors Cited by (11) Tweetations Metrics

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  8. Sabaner M, Yozgat Z. Performance of ChatGPT-4 Omni and Gemini 1.5 Pro on Ophthalmology-Related Questions in the Turkish Medical Specialty Exam. Turkish Journal of Ophthalmology 2025;55(4):177 View
  9. Ming S, Yao X, Guo Q, Chen D, Guo X, Xie K, Lei B. Evaluation of DeepSeek-R1 for Ophthalmic Diagnosis and Reasoning: A Comparison with OpenAI o1 and o3. Journal of Medical Systems 2025;49(1) View
  10. Zhang Z, Zhang H, Pan Z, Bi Z, Wan Y, Song X, Fan X. Evaluating Large Language Models in Ophthalmology: Systematic Review. Journal of Medical Internet Research 2025;27:e76947 View
  11. 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