September 19, 2025

Harmony Thrive

Superior Health, Meaningful Life

Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators

Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators
  • Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29:1930–40. https://doi.org/10.1038/s41591-023-02448-8.

    Article 
    PubMed 

    Google Scholar 

  • Cascella M, Semeraro F, Montomoli J, Bellini V, Piazza O, Bignami E. The breakthrough of large language models release for medical applications: 1-year timeline and perspectives. J Med Syst. 2024;48:22. https://doi.org/10.1007/s10916-024-02045-3.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9:e46885. https://doi.org/10.2196/46885.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zandi R, Fahey JD, Drakopoulos M, Bryan JM, Dong S, Bryar PJ, et al. Exploring diagnostic precision and triage proficiency: a comparative study of GPT-4 and bard in addressing common ophthalmic complaints. Bioengineering. 2024;11:120. https://doi.org/10.3390/bioengineering11020120.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lyons RJ, Arepalli SR, Fromal O, Choi JD, Jain N. Artificial intelligence chatbot performance in triage of ophthalmic conditions. Can J Ophthalmol. 2024;59:e301–e308. https://doi.org/10.1016/j.jcjo.2023.07.016.

    Article 
    PubMed 

    Google Scholar 

  • Singh S, Djalilian A, Ali MJ. ChatGPT and ophthalmology: exploring its potential with discharge summaries and operative notes. Semin Ophthalmol. 2023;38:503–7. https://doi.org/10.1080/08820538.2023.2209166.

    Article 
    PubMed 

    Google Scholar 

  • Gopalakrishnan N, Joshi A, Chhablani J, Yadav NK, Reddy NG, Rani PK, et al. Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios. Int J Retin Vitreous. 2024;10:11. https://doi.org/10.1186/s40942-024-00533-9.

    Article 

    Google Scholar 

  • Choudhary A, Gopalakrishnan N, Joshi A, Balakrishnan D, Chhablani J, Yadav NK, et al. Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms. Int J Retin Vitreous. 2024;10:22. https://doi.org/10.1186/s40942-024-00544-6.

    Article 

    Google Scholar 

  • Liu X, Wu J, Shao A, Shen W, Ye P, Wang Y, et al. Uncovering language disparity of ChatGPT on retinal vascular disease classification: cross-sectional study. J Med Internet Res. 2024;26:e51926. https://doi.org/10.2196/51926.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mohammadi SS, Nguyen QD. A user-friendly approach for the diagnosis of diabetic retinopathy using ChatGPT and automated machine learning. Ophthalmol Sci. 2024;4:100495. https://doi.org/10.1016/j.xops.2024.100495.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen X, Zhang W, Xu P, Zhao Z, Zheng Y, Shi D, et al. FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer. NPJ Digit Med. 2024;7:111. https://doi.org/10.1038/s41746-024-01101-z.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen X, Zhang W, Zhao Z, Xu P, Zheng Y, Shi D, et al. ICGA-GPT: report generation and question answering for indocyanine green angiography images. Br J Ophthalmol. 2024;108:1450–6. https://doi.org/10.1136/bjo-2023-324446.

    Article 
    PubMed 

    Google Scholar 

  • Lin Z, Zhang D, Shi D, Xu R, Tao Q, Wu L, et al. Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation. J Biomed Inf. 2023;138:104281. https://doi.org/10.1016/j.jbi.2023.104281.

    Article 

    Google Scholar 

  • Chen X, Xu P, Li Y, Zhang W, Song F, He M, et al. ChatFFA: an ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography. iScience. 2024;27:110021. https://doi.org/10.1016/j.isci.2024.110021.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Carlà MM, Gambini G, Baldascino A, Giannuzzi F, Boselli F, Crincoli E, et al. Exploring AI-chatbots’ capability to suggest surgical planning in ophthalmology: ChatGPT versus Google Gemini analysis of retinal detachment cases. Br J Ophthalmol. 2024;108:1457–69. https://doi.org/10.1136/bjo-2023-325143.

    Article 
    PubMed 

    Google Scholar 

  • Huang X, Raja H, Madadi Y, Delsoz M, Poursoroush A, Kahook MY, et al. Predicting glaucoma before onset using a large language model chatbot. Am J Ophthalmol. 2024;266:289–99. https://doi.org/10.1016/j.ajo.2024.05.022.

    Article 
    PubMed 

    Google Scholar 

  • Kass MA, Heuer DK, Higginbotham EJ, Johnson CA, Keltner JL, Miller JP, et al. The ocular hypertension treatment study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol. 2002;120:701–13. https://doi.org/10.1001/archopht.120.6.701.

    Article 
    PubMed 

    Google Scholar 

  • Carlà MM, Gambini G, Baldascino A, Boselli F, Giannuzzi F, Margollicci F, et al. Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison. Graefes Arch Clin Exp Ophthalmol. 2024;262:2945–59. https://doi.org/10.1007/s00417-024-06470-5.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rojas-Carabali W, Sen A, Agarwal A, Tan G, Cheung CY, Rousselot A, et al. Chatbots Vs. human experts: evaluating diagnostic performance of chatbots in uveitis and the perspectives on AI adoption in ophthalmology. Ocul Immunol Inflamm. 2024;32:1591–8. https://doi.org/10.1080/09273948.2023.2266730.

    Article 
    PubMed 

    Google Scholar 

  • Ćirković A, Katz T. Exploring the potential of ChatGPT-4 in predicting refractive surgery categorizations: comparative study. JMIR Form Res. 2023;7:e51798. https://doi.org/10.2196/51798.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ali MJ. ChatGPT and lacrimal drainage disorders: performance and scope of improvement. Ophthalmic Plast Reconstr Surg. 2023;39:221–5. https://doi.org/10.1097/IOP.0000000000002418.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tailor PD, Dalvin LA, Chen JJ, Iezzi R, Olsen TW, Scruggs BA, et al. A comparative study of responses to retina questions from either experts, expert-edited large language models, or expert-edited large language models alone. Ophthalmol Sci. 2024;4:100485. https://doi.org/10.1016/j.xops.2024.100485.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pushpanathan K, Lim ZW, Er Yew SM, Chen DZ, Hui’En Lin HA, Lin Goh JH, et al. Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries. iScience. 2023;26:108163. https://doi.org/10.1016/j.isci.2023.108163.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tailor PD, Xu TT, Fortes BH, Iezzi R, Olsen TW, Starr MR, et al. Appropriateness of ophthalmology recommendations from an online chat-based artificial intelligence model. Mayo Clin Proc Digit Health. 2024;2:119–28. https://doi.org/10.1016/j.mcpdig.2024.01.003.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Barclay KS, You JY, Coleman MJ, Mathews PM, Ray VL, Riaz KM, et al. Quality and agreement with scientific consensus of ChatGPT information regarding corneal transplantation and Fuchs dystrophy. Cornea. 2024;43:746–50. https://doi.org/10.1097/ICO.0000000000003439.

    Article 
    PubMed 

    Google Scholar 

  • Kianian R, Sun D, Crowell EL, Tsui E. The use of large language models to generate education materials about uveitis. Ophthalmol Retin. 2024;8:195–201. https://doi.org/10.1016/j.oret.2023.09.008.

    Article 

    Google Scholar 

  • Dihan Q, Chauhan MZ, Eleiwa TK, Hassan AK, Sallam AB, Khouri AS, et al. Using large language models to generate educational materials on childhood glaucoma. Am J Ophthalmol. 2024;265:28–38. https://doi.org/10.1016/j.ajo.2024.04.004.

    Article 
    PubMed 

    Google Scholar 

  • Ferro Desideri L, Roth J, Zinkernagel M, Anguita R. Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration. Int J Retin Vitreous. 2023;9:71. https://doi.org/10.1186/s40942-023-00511-7.

    Article 

    Google Scholar 

  • Wu G, Zhao W, Wong A, Lee DA. Patients with floaters: answers from virtual assistants and large language models. Digit Health. 2024;10:20552076241229933. https://doi.org/10.1177/20552076241229933.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Milad D, Antaki F, Milad J, Farah A, Khairy T, Mikhail D, et al. Assessing the medical reasoning skills of GPT-4 in complex ophthalmology cases. Br J Ophthalmol. 2024;108:1398–405. https://doi.org/10.1136/bjo-2023-325053.

    Article 
    PubMed 

    Google Scholar 

  • Antaki F, Milad D, Chia MA, Giguère CÉ, Touma S, El-Khoury J, et al. Capabilities of GPT-4 in ophthalmology: an analysis of model entropy and progress towards human-level medical question answering. Br J Ophthalmol. 2024;108:1371–8. https://doi.org/10.1136/bjo-2023-324438.

    Article 
    PubMed 

    Google Scholar 

  • Antaki F, Touma S, Milad D, El-Khoury J, Duval R. Evaluating the performance of ChatGPT in ophthalmology: an analysis of its successes and shortcomings. Ophthalmol Sci. 2023;3:100324. https://doi.org/10.1016/j.xops.2023.100324.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Botross M, Mohammadi SO, Montgomery K, Crawford C. Performance of Google’s artificial intelligence chatbot “Bard” (now “Gemini”) on ophthalmology board exam practice questions. Cureus. 2024;16:e57348. https://doi.org/10.7759/cureus.57348.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Haddad F, Saade JS. Performance of ChatGPT on ophthalmology-related questions across various examination levels: observational study. JMIR Med Educ. 2024;10:e50842. https://doi.org/10.2196/50842.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fowler T, Pullen S, Birkett L. Performance of ChatGPT and Bard on the official part 1 FRCOphth practice questions. Br J Ophthalmol. 2024;108:1379–83. https://doi.org/10.1136/bjo-2023-324091.

    Article 
    PubMed 

    Google Scholar 

  • Thirunavukarasu AJ, Mahmood S, Malem A, Foster WP, Sanghera R, Hassan R, et al. Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: a head-to-head cross-sectional study. PLOS Digit Health. 2024;3:e0000341. https://doi.org/10.1371/journal.pdig.0000341.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Panthier C, Gatinel D. Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: a novel approach to medical knowledge assessment. J Fr Ophtalmol. 2023;46:706–11. https://doi.org/10.1016/j.jfo.2023.05.006.

    Article 
    PubMed 

    Google Scholar 

  • Sakai D, Maeda T, Ozaki A, Kanda GN, Kurimoto Y, Takahashi M. Performance of ChatGPT in board examinations for specialists in the Japanese Ophthalmology Society. Cureus. 2023;15:e49903. https://doi.org/10.7759/cureus.49903.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cai LZ, Shaheen A, Jin A, Fukui R, Yi JS, Yannuzzi N, et al. Performance of generative large language models on ophthalmology board-style questions. Am J Ophthalmol. 2023;254:141–9. https://doi.org/10.1016/j.ajo.2023.05.024.

    Article 
    PubMed 

    Google Scholar 

  • Jiao C, Edupuganti NR, Patel PA, Bui T, Sheth V. Evaluating the artificial intelligence performance growth in ophthalmic knowledge. Cureus. 2023;15:e45700. https://doi.org/10.7759/cureus.45700.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Moshirfar M, Altaf AW, Stoakes IM, Tuttle JJ, Hoopes PC. Artificial intelligence in ophthalmology: a comparative analysis of GPT-3.5, GPT-4, and human expertise in answering StatPearls questions. Cureus. 2023;15:e40822. https://doi.org/10.7759/cureus.40822.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lim ZW, Pushpanathan K, Yew SME, Lai Y, Sun CH, Lam JSH, et al. Benchmarking large language models’ performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard. EBioMedicine. 2023;95:104770. https://doi.org/10.1016/j.ebiom.2023.104770.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Marshall RF, Mallem K, Xu H, Thorne J, Burkholder B, Chaon B, et al. Investigating the accuracy and completeness of an artificial intelligence large language model about uveitis: an evaluation of ChatGPT. Ocul Immunol Inflamm. 2024;32:2052–5. https://doi.org/10.1080/09273948.2024.2317417.

    Article 
    PubMed 

    Google Scholar 

  • Delsoz M, Raja H, Madadi Y, Tang AA, Wirostko BM, Kahook MY, et al. The use of ChatGPT to assist in diagnosing glaucoma based on clinical case reports. Ophthalmol Ther. 2023;12:3121–32. https://doi.org/10.1007/s40123-023-00805-x.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Taloni A, Borselli M, Scarsi V, Rossi C, Coco G, Scorcia V, et al. Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the self-assessment program of American Academy of Ophthalmology. Sci Rep. 2023;13:18562. https://doi.org/10.1038/s41598-023-45837-2.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Singer MB, Fu JJ, Chow J, Teng CC. Development and evaluation of aeyeconsult: a novel ophthalmology Chatbot leveraging verified textbook knowledge and GPT-4. J Surg Educ. 2024;81:438–43. https://doi.org/10.1016/j.jsurg.2023.11.019.

    Article 
    PubMed 

    Google Scholar 

  • Raja H, Munawar A, Mylonas N, Delsoz M, Madadi Y, Elahi M, et al. Automated category and trend analysis of scientific articles on ophthalmology using large language models: development and usability study. JMIR Form Res. 2024;8:e52462. https://doi.org/10.2196/52462.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dupps WJ Jr. Artificial intelligence and academic publishing. J Cataract Refract Surg. 2023;49:655–6. https://doi.org/10.1097/j.jcrs.0000000000001223.

    Article 
    PubMed 

    Google Scholar 

  • Van Gelder RN. The pros and cons of artificial intelligence authorship in ophthalmology. Ophthalmology. 2023;130:670–1. https://doi.org/10.1016/j.ophtha.2023.05.018.

    Article 
    PubMed 

    Google Scholar 

  • Bressler NM. What artificial intelligence chatbots mean for editors, authors, and readers of peer-reviewed ophthalmic literature. JAMA Ophthalmol. 2023;141:514–5. https://doi.org/10.1001/jamaophthalmol.2023.1370.

    Article 
    PubMed 

    Google Scholar 

  • Apellis Pharmaceuticals. FDA approves Syfovre (pegcetacoplan) injection, the first and only in its class. 2023. Available at: Accessed August 18, 2024.

  • EyesOnEyeCare. FDA approves IVERIC bio’s IZERVAY (branciciclovir injection) for geographic atrophy. 2023. Available at: Accessed August 18, 2024.

  • Volpe NJ, Mirza RG. Chatbots, artificial intelligence, and the future of scientific reporting. JAMA Ophthalmol. 2023;141:824–5. https://doi.org/10.1001/jamaophthalmol.2023.3344.

    Article 
    PubMed 

    Google Scholar 

  • Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, et al. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv. 2022, https://arxiv.org/abs/2201.11903.

  • Anisuzzaman DM, Malins JG, Friedman PA, Attia ZI. Fine-tuning large language models for specialized use cases. Mayo Clin Proc Digit Health. 2024;3:100184. https://doi.org/10.1016/j.mcpdig.2024.11.005.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al. Training language models to follow instructions with human feedback. In: Proceedings of the Neural Information Processing Systems (NeurIPS) 2022; 2022. https://doi.org/10.48550/arXiv.2203.02155.

  • Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Proceedings of the 36th International Conference on Machine Learning. 2019:5243–52. https://doi.org/10.5555/3495724.3496517.

  • Nguyen Q, Nguyen DA, Dang K, Liu S, Nguyen K, Wang SY, et al. Advancing question-answering in ophthalmology with retrieval-augmented generation (RAG): Benchmarking open-source and proprietary large language models. J-GLOBAL. 2024. Available from: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402211872512470.

  • Chen JS, Reddy AJ, Al-Sharif E, Shoji MK, Kalaw FGP, Eslani M, et al. Analysis of ChatGPT responses to ophthalmic cases: can ChatGPT think like an ophthalmologist. Ophthalmol Sci. 2024;5:100600. https://doi.org/10.1016/j.xops.2024.100600.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ullah E, Parwani A, Baig MM, Singh R. Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology – a recent scoping review. Diagn Pathol. 2024;19:43. https://doi.org/10.1186/s13000-024-01464-7.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Celi LA, Cellini J, Charpignon ML, Dee EC, Dernoncourt F, Eber R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities-a global review. PLOS Digit Health. 2022;1:e0000022. https://doi.org/10.1371/journal.pdig.0000022.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dychiao RGK, Alberto IRI, Artiaga JCM, Salongcay RP, Celi LA. Large language model integration in Philippine ophthalmology: early challenges and steps forward. Lancet Digit Health. 2024;6:e308. https://doi.org/10.1016/S2589-7500(24)00064-5.

    Article 
    PubMed 

    Google Scholar 

  • Restrepo D, Wu C, Tang Z, Shuai Z, Phan TNM, Ding J-E, et al. Multi-OphthaLingua: a multilingual benchmark for assessing and debiasing LLM ophthalmological QA in LMICs. AAAI. 2025;39:28321–30.

    Google Scholar 

  • Tom E, Keane PA, Blazes M, Pasquale LR, Chiang MF, Lee AY, et al. Protecting data privacy in the age of AI-enabled ophthalmology. Transl Vis Sci Technol. 2020;9:36. https://doi.org/10.1167/tvst.9.2.36.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kalaw FGP, Baxter SL. Ethical considerations for large language models in ophthalmology. Curr Opin Ophthalmol. 2024;35:438–46. https://doi.org/10.1097/ICU.0000000000001083.

    Article 
    PubMed 

    Google Scholar 

  • Bernstein IA, Zhang YV, Govil D, Majid I, Chang RT, Sun Y, et al. Comparison of ophthalmologist and large language model chatbot responses to online patient eye care questions. JAMA Netw Open. 2023;6:e2330320. https://doi.org/10.1001/jamanetworkopen.2023.30320.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cohen SA, Brant A, Fisher AC, Pershing S, Do D, Pan C. Dr. Google vs. Dr. ChatGPT: exploring the use of artificial intelligence in ophthalmology by comparing the accuracy, safety, and readability of responses to frequently asked patient questions regarding cataracts and cataract surgery. Semin Ophthalmol. 2024;39:472–9. https://doi.org/10.1080/08820538.2024.2326058.

    Article 
    PubMed 

    Google Scholar 

  • Wilhelm TI, Roos J, Kaczmarczyk R. Large language models for therapy recommendations across 3 clinical specialties: comparative study. J Med Internet Res. 2023;25:e49324. https://doi.org/10.2196/49324.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Xue X, Zhang D, Sun C, Shi Y, Wang R, Tan T, et al. Xiaoqing: A Q&A model for glaucoma based on LLMs. Comput Biol Med. 2024;174:108399. https://doi.org/10.1016/j.compbiomed.2024.108399.

    Article 
    PubMed 

    Google Scholar 

  • Biswas S, Logan NS, Davies LN, Sheppard AL, Wolffsohn JS. Assessing the utility of ChatGPT as an artificial intelligence-based large language model for information to answer questions on myopia. Ophthalmic Physiol Opt. 2023;43:1562–70. https://doi.org/10.1111/opo.13207.

    Article 
    PubMed 

    Google Scholar 

  • link

    Leave a Reply

    Your email address will not be published. Required fields are marked *

    Copyright © All rights reserved. | Newsphere by AF themes.