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Chatbots and Conversational Agents (1173) eHealth Literacy / Digital Literacy (879) Ophthalmology (117) Generative Language Models Including ChatGPT (1506)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59843, first published .
Hands typing on a laptop displaying the ChatGPT interface, showing examples and capabilities.

Large Language Models May Help Patients Understand Peer-Reviewed Scientific Articles About Ophthalmology: Development and Usability Study

Large Language Models May Help Patients Understand Peer-Reviewed Scientific Articles About Ophthalmology: Development and Usability Study

Authors of this article:

Reza Kianian1 Author Orcid Image ;   Deyu Sun1 Author Orcid Image ;   William Rojas-Carabali2, 3, 4 Author Orcid Image ;   Rupesh Agrawal2, 3, 4 Author Orcid Image ;   Edmund Tsui1 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

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  2. Qiang S, Zhang H, Liao Y, Zhang Y, Gu Y, Wang Y, Xu Z, Shi H, Han N, Yu H. Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study. Journal of Medical Internet Research 2025;27:e73226 View
  3. Daram N, Maxwell R, D'Amato J, Massengill J. Can artificial intelligence improve the readability of patient education information in gynecology?. American Journal of Obstetrics and Gynecology 2025;233(6):640.e1 View
  4. Alharbi L, Alrashoud R, Alotaibi B, Al Dera A, Alajlan R, AlHuthail R, Alessa D. Using Artificial Intelligence ChatGPT to Access Medical Information About Chemical Eye Injuries: Comparative Study. JMIR Formative Research 2025;9:e73642 View
  5. Romanyukha A, Mazloumi M, Waelheyns T, Mishra N, Jacobs J, Fitousi N. Development of a context-aware integrated training module based on large language models for continuous education in radiation protection. Physica Medica 2025;137:105090 View
  6. Mendoza-Pinto C, Munguía-Realpozo P, Etchegaray-Morales I, Ramírez-Lara E, Solis-Poblano J, García-Flores M, Ayón-Aguilar J. Artificial intelligence in patient education: evaluating large language models for understanding rheumatology literature. Frontiers in Digital Health 2025;7 View
  7. Bai J, Ji X, Yu J, Wang Y, Guo Y, Xue C, Zhang W, Zhu J. Assessing the Quality of AI Responses to Patient Concerns About Axial Spondyloarthritis: Delphi-Based Evaluation. JMIR AI 2026;5:e79153 View
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  9. Meng J, Dai R, Huang X, Gu Y, Yan S, Wang X, Gao J, Zhang T. Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study. Journal of Medical Internet Research 2025;27:e83219 View
  10. Gao A, Butt A, Min F, Hatamnejad A, Nanji K, Gulamhusein H. Assessing demographic variation in large language model outputs for patient education materials in cataract surgery. AJO International 2026;3(1):100216 View
  11. Chi M, Cui Y, Xi L. Advances in the application of artificial intelligence in ophthalmic education and clinical training. Frontiers in Medicine 2026;12 View
  12. Jones M, Torgbi M, Tayyar Madabushi H. Improving the Understandability of Clinical Guidelines: Development and Evaluation of a GPT-4–Based Pipeline. Journal of Medical Internet Research 2026;28:e81915 View
  13. Tvrda L, Burton J, McConnell K, Mavromati K, Knoche H, Mikulik R, Quinn T. Information leaflets vs artificial intelligence: comparing perceptions of stroke survivors and professionals in a mixed-methods study. European Stroke Journal 2026;11(4) View
  14. Hu M, Wang Z, Zhang Z, Li M. Challenges of patient-facing generative artificial intelligence in hypertension care: A cross-platform evaluation of the quality, readability, and actionability of LLM-Generated patient education materials. DIGITAL HEALTH 2026;12 View
  15. Luo Y, Ge T, Luo X, Lin Z, Li M, Ke B. Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background. Frontiers in Medicine 2026;13 View
  16. Yin H, Yu T, Wu W, Shen L, Shi D, Chen Y, Zhao K, Grzybowski A, Jin K. Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma. Frontiers in Medicine 2026;13 View
  17. Sachdeva K, Chaudry E, Butt F, Wright G, Dhawan A, Bhatti A. Systematic review and meta-analysis of the readability of large language models for patient education on ophthalmological conditions. Canadian Journal of Ophthalmology 2026 View
  18. Jiang M, Zhou M, Wan X, Zhang J. Large Language Model Simplification of Open Access Pediatric Strabismus Literature: Cross-Sectional Validation of Readability and Clinical Fidelity. JMIR Formative Research 2026;10:e91572 View