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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (86) Chatbots and Conversational Agents (543) Generative Language Models Including ChatGPT (325) Artificial Intelligence (1421) Artificial Intelligence (AI) in Medical Education (83) Artificial Intelligence (96)

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 (12) 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 2024 View