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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (102) Chatbots and Conversational Agents (635) Generative Language Models Including ChatGPT (501) Artificial Intelligence (1745) Artificial Intelligence (AI) in Medical Education (125)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50842, first published .
Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study

Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study

Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study

Authors of this article:

Firas Haddad1 Author Orcid Image ;   Joanna S Saade2 Author Orcid Image
Article Authors Cited by (18) Tweetations (2) Metrics

Journals

  1. Noda M, Ueno T, Koshu R, Takaso Y, Shimada M, Saito C, Sugimoto H, Fushiki H, Ito M, Nomura A, Yoshizaki T. Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study. JMIR Medical Education 2024;10:e57054 View
  2. Botross M, Mohammadi S, Montgomery K, Crawford C. Performance of Google’s Artificial Intelligence Chatbot “Bard” (Now “Gemini”) on Ophthalmology Board Exam Practice Questions. Cureus 2024 View
  3. Ogunleye B, Zakariyyah K, Ajao O, Olayinka O, Sharma H. A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences 2024;14(6):636 View
  4. Kenney R, Requarth T, Jack A, Hyman S, Galetta S, Grossman S. AI in Neuro-Ophthalmology: Current Practice and Future Opportunities. Journal of Neuro-Ophthalmology 2024;44(3):308 View
  5. Yang Z, Wang D, Zhou F, Song D, Zhang Y, Jiang J, Kong K, Liu X, Qiao Y, Chang R, Han Y, Li F, Tham C, Zhang X. Understanding natural language: Potential application of large language models to ophthalmology. Asia-Pacific Journal of Ophthalmology 2024;13(4):100085 View
  6. Şensoy E, Çıtırık M. Okülofasiyal Plastik ve Orbital Cerrahide İngilizce ve Türkçe Dil Çeşitliliğinin Yapay Zeka Chatbot Performansına Etkisi: ChatGPT-3.5, Copilot ve Gemini Üzerine Bir Çalışma. OSMANGAZİ JOURNAL OF MEDICINE 2024;46(5) View
  7. Sabaner M, Hashas A, Mutibayraktaroglu K, Yozgat Z, Klefter O, Subhi Y. The performance of artificial intelligence-based large language models on ophthalmology-related questions in Swedish proficiency test for medicine: ChatGPT-4 omni vs Gemini 1.5 Pro. AJO International 2024;1(4):100070 View
  8. Wu J, Nishida T, Liu T. Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis. Asia-Pacific Journal of Ophthalmology 2024;13(5):100106 View
  9. Bellanda V, Santos M, Ferraz D, Jorge R, Melo G. Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review. International Journal of Retina and Vitreous 2024;10(1) View
  10. Wang D, Liang J, Ye J, Li J, Li J, Zhang Q, Hu Q, Pan C, Wang D, Liu Z, Shi W, Shi D, Li F, Qu B, Zheng Y. Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study. Journal of Medical Internet Research 2024;26:e58041 View
  11. Gill G, Blair J, Litinsky S. Evaluating the Performance of ChatGPT 3.5 and 4.0 on StatPearls Oculoplastic Surgery Text- and Image-Based Exam Questions. Cureus 2024 View
  12. Mavrych V, Ganguly P, Bolgova O. Using large language models (ChatGPT, Copilot, PaLM, Bard, and Gemini) in Gross Anatomy course: Comparative analysis. Clinical Anatomy 2025;38(2):200 View
  13. Penny P, Bane R, Riddle V. Advancements in AI Medical Education: Assessing ChatGPT’s Performance on USMLE-Style Questions Across Topics and Difficulty Levels. Cureus 2024 View
  14. Sabaner M, Anguita R, Antaki F, Balas M, Boberg-Ans L, Ferro Desideri L, Grauslund J, Hansen M, Klefter O, Potapenko I, Rasmussen M, Subhi Y. Opportunities and Challenges of Chatbots in Ophthalmology: A Narrative Review. Journal of Personalized Medicine 2024;14(12):1165 View
  15. Lin T, Lin R, Mhaskar R, Margo C. Evaluating the Accuracy of Advanced Language Learning Models in Ophthalmology: A Comparative Study of ChatGPT-4o and Meta AI’s Llama 3.1. Advances in Ophthalmology Practice and Research 2025 View
  16. Şensoy E, Çıtırık M. Oftalmik Patolojiler ve Göz İçi Tümörlerinde Dil Farklılıklarının Yapay Zeka Chatbot Performansı Üzerindeki Etkisinin Değerlendirilmesi: ChatGPT-3.5, Copilot ve Gemini Üzerine Bir Çalışma. Harran Üniversitesi Tıp Fakültesi Dergisi 2025;22(1):61 View
  17. Srinivasan S, Ji H, Chen D, Wong W, Soh Z, Goh J, Pushpanathan K, Wang X, Ma W, Wong T, Wang Y, Cheng C, Tham Y. Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs?. BMJ Open Ophthalmology 2025;10(1):e002076 View
  18. Wang L, Li J, Zhuang B, Huang S, Fang M, Wang C, Li W, Zhang M, Gong S. Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis. Journal of Medical Internet Research 2025;27:e64486 View