{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:17:09Z","timestamp":1785151029115,"version":"3.55.0"},"reference-count":88,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The advancement of generative artificial intelligence (AI) has resulted in its use permeating many areas of life. Amidst this eruption of scientific output, a wide range of research regarding the usage of Large Language Models (LLMs) in ophthalmology has emerged. In this study, we aim to map out the landscape of LLM applications in ophthalmology, and by consolidating the work carried out, we aim to produce a point of reference to guide the conduct of future works. Eight databases were searched for articles from 2019 to 2024. In total, 976 studies were screened, and a final 49 were included. The study designs and outcomes of these studies were analysed. The performance of LLMs was further analysed in the areas of exam taking and patient education, diagnostic capability, management capability, administration, inaccuracies, and harm. LLMs performed acceptably in most studies, even surpassing humans in some. Despite their relatively good performance, issues pertaining to study design, grading protocols, hallucinations, inaccuracies, and harm were found to be pervasive. LLMs have received considerable attention through their introduction to the public and have found potential applications in the field of medicine, and in particular, ophthalmology. However, using standardised evaluation frameworks and addressing gaps in the current literature when applying LLMs in ophthalmology is recommended through this review.<\/jats:p>","DOI":"10.3390\/bdcc9060151","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T09:43:14Z","timestamp":1749202994000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["The Use of Large Language Models in Ophthalmology: A Scoping Review on Current Use-Cases and Considerations for Future Works in This Field"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1757-883X","authenticated-orcid":false,"given":"Ye King Clarence","family":"See","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Tan Tock Seng Hospital, Singapore 308433, Singapore"},{"name":"National Healthcare Group Eye Institution, Singapore 308433, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3841-3479","authenticated-orcid":false,"given":"Khai Shin Alva","family":"Lim","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei Yung","family":"Au","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si Yin Charlene","family":"Chia","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1223-9986","authenticated-orcid":false,"given":"Xiuyi","family":"Fan","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore"},{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore"},{"name":"Centre for Medical Technologies & Innovations, National Health Group, Singapore 138543, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-5118","authenticated-orcid":false,"given":"Zhenghao Kelvin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Tan Tock Seng Hospital, Singapore 308433, Singapore"},{"name":"National Healthcare Group Eye Institution, Singapore 308433, Singapore"},{"name":"Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA 94303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G.P., Ferragina, P., Tozzi, A.E., and Rizzo, C. (2023). ChatGPT and the rise of large language models: The new AI-driven infodemic threat in public health. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1166120"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1001\/jama.2023.5321","article-title":"AI-Generated Medical Advice-GPT and Beyond","volume":"329","author":"Haupt","year":"2023","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepa\u00f1o, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., and Maningo, J. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit. Health, 2.","DOI":"10.1371\/journal.pdig.0000198"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Z., He, X., Liu, L., Liu, T., and Zhai, X. (2023). Context Matters: A Strategy to Pre-train Language Model for Science Education. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, Springer Nature.","DOI":"10.1007\/978-3-031-36336-8_103"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1111\/aos.15661","article-title":"Artificial intelligence-based chatbot patient information on common retinal diseases using ChatGPT","volume":"101","author":"Potapenko","year":"2023","journal-title":"Acta Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e46599","DOI":"10.2196\/46599","article-title":"Trialling a Large Language Model (ChatGPT) in General Practice With the Applied Knowledge Test: Observational Study Demonstrating Opportunities and Limitations in Primary Care","volume":"9","author":"Thirunavukarasu","year":"2023","journal-title":"JMIR Med. Educ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e917","DOI":"10.1016\/S2589-7500(23)00201-7","article-title":"Large language models and their impact in ophthalmology","volume":"5","author":"Betzler","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1136\/bjophthalmol-2022-321141","article-title":"New meaning for NLP: The trials and tribulations of natural language processing with GPT-3 in ophthalmology","volume":"106","author":"Nath","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4103\/tjo.TJO-D-23-00012","article-title":"Application of big data in ophthalmology","volume":"13","author":"Soh","year":"2023","journal-title":"Taiwan J. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1136\/bjo-2023-324734","article-title":"Review of emerging trends and projection of future developments in large language models research in ophthalmology","volume":"108","author":"Wong","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jin, K., Yuan, L., Wu, H., Grzybowski, A., and Ye, J. (2023). Exploring large language model for next generation of artificial intelligence in ophthalmology. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1291404"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13063-020-04951-6","article-title":"Reporting guidelines for clinical trials of artificial intelligence interventions: The SPIRIT-AI and CONSORT-AI guidelines","volume":"22","author":"Ibrahim","year":"2021","journal-title":"Trials"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: Towards a methodological framework","volume":"8","author":"Arksey","year":"2005","journal-title":"Int. J. Soc. Res. Methodol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1097\/IOP.0000000000002418","article-title":"ChatGPT and Lacrimal Drainage Disorders: Performance and Scope of Improvement","volume":"39","author":"Ali","year":"2023","journal-title":"Ophthalmic Plast. Reconstr. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1097\/IOP.0000000000002567","article-title":"Evaluating the Accuracy of ChatGPT and Google BARD in Fielding Oculoplastic Patient Queries: A Comparative Study on Artificial versus Human Intelligence","volume":"40","author":"Penteado","year":"2024","journal-title":"Ophthalmic Plast. Reconstr. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1136\/bjo-2023-324438","article-title":"Capabilities of GPT-4 in ophthalmology: An analysis of model entropy and progress towards human-level medical question answering","volume":"108","author":"Antaki","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100324","DOI":"10.1016\/j.xops.2023.100324","article-title":"Evaluating the Performance of ChatGPT in Ophthalmology: An Analysis of Its Successes and Shortcomings","volume":"3","author":"Antaki","year":"2023","journal-title":"Ophthalmol. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1097\/IOP.0000000000002552","article-title":"Evaluating ChatGPT on Orbital and Oculofacial Disorders: Accuracy and Readability Insights","volume":"40","author":"Balas","year":"2024","journal-title":"Ophthalmic Plast. Reconstr. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1097\/ICO.0000000000003439","article-title":"Quality and Agreement With Scientific Consensus of ChatGPT Information Regarding Corneal Transplantation and Fuchs Dystrophy","volume":"43","author":"Barclay","year":"2024","journal-title":"Cornea"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e2330320","DOI":"10.1001\/jamanetworkopen.2023.30320","article-title":"Comparison of Ophthalmologist and Large Language Model Chatbot Responses to Online Patient Eye Care Questions","volume":"6","author":"Bernstein","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1111\/opo.13207","article-title":"Assessing the utility of ChatGPT as an artificial intelligence-based large language model for information to answer questions on myopia","volume":"43","author":"Biswas","year":"2023","journal-title":"Ophthalmic Physiol. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ajo.2023.05.024","article-title":"Performance of Generative Large Language Models on Ophthalmology Board-Style Questions","volume":"254","author":"Cai","year":"2023","journal-title":"Am. J. Ophthalmol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1038\/s41433-023-02906-0","article-title":"Reliability and accuracy of artificial intelligence ChatGPT in providing information on ophthalmic diseases and management to patients","volume":"38","author":"Cappellani","year":"2024","journal-title":"Eye"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e51798","DOI":"10.2196\/51798","article-title":"Exploring the Potential of ChatGPT-4 in Predicting Refractive Surgery Categorizations: Comparative Study","volume":"7","author":"Katz","year":"2023","journal-title":"JMIR Form. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1007\/s40123-023-00805-x","article-title":"The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports","volume":"12","author":"Delsoz","year":"2023","journal-title":"Ophthalmol. Ther."},{"key":"ref_27","first-page":"389","article-title":"Assessing the Competence of Artificial Intelligence Programs in Pediatric Ophthalmology and Strabismus and Comparing their Relative Advantages","volume":"67","author":"Sensoy","year":"2023","journal-title":"Rom. J. Ophthalmol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1097\/IOP.0000000000002549","article-title":"Optimizing Ophthalmology Patient Education via ChatBot-Generated Materials: Readability Analysis of AI-Generated Patient Education Materials and The American Society of Ophthalmic Plastic and Reconstructive Surgery Patient Brochures","volume":"40","author":"Eid","year":"2024","journal-title":"Ophthalmic Plast. Reconstr. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s40942-023-00511-7","article-title":"Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration","volume":"9","author":"Roth","year":"2023","journal-title":"Int. J. Retin. Vitr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1136\/bjo-2023-324091","article-title":"Performance of ChatGPT and Bard on the official part 1 FRCOphth practice questions","volume":"108","author":"Fowler","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e50842","DOI":"10.2196\/50842","article-title":"Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study","volume":"10","author":"Haddad","year":"2024","journal-title":"JMIR Med. Educ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1167\/tvst.11.3.37","article-title":"Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers","volume":"11","author":"Hu","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1001\/jamaophthalmol.2023.3119","article-title":"Evaluation and Comparison of Ophthalmic Scientific Abstracts and References by Current Artificial Intelligence Chatbots","volume":"141","author":"Hua","year":"2023","journal-title":"JAMA Ophthalmol."},{"key":"ref_34","first-page":"e45700","article-title":"Evaluating the Artificial Intelligence Performance Growth in Ophthalmic Knowledge","volume":"15","author":"Jiao","year":"2023","journal-title":"Cureus"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.oret.2023.09.008","article-title":"The Use of Large Language Models to Generate Education Materials about Uveitis","volume":"8","author":"Kianian","year":"2024","journal-title":"Ophthalmol. Retin."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1097\/IJG.0000000000002338","article-title":"Can ChatGPT Aid Clinicians in Educating Patients on the Surgical Management of Glaucoma?","volume":"33","author":"Kianian","year":"2024","journal-title":"J. Glaucoma"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lim, Z.W., Pushpanathan, K., Yew, S.M.E., Lai, Y., Sun, C.H., Lam, J.S.H., Chen, D.Z., Goh, J.H.L., Tan, M.C.J., and Sheng, B. (2023). Benchmarking large language models\u2019 performances for myopia care: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard. EBioMedicine, 95.","DOI":"10.1016\/j.ebiom.2023.104770"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e51926","DOI":"10.2196\/51926","article-title":"Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study","volume":"26","author":"Liu","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e301","DOI":"10.1016\/j.jcjo.2023.07.016","article-title":"Artificial intelligence chatbot performance in triage of ophthalmic conditions","volume":"59","author":"Lyons","year":"2024","journal-title":"Can. J. Ophthalmol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1097\/IAE.0000000000004053","article-title":"Performance Assessment of An Artificial Intelligence Chatbot in Clinical Vitreoretinal Scenarios","volume":"44","author":"Maywood","year":"2024","journal-title":"Retina"},{"key":"ref_41","first-page":"e40822","article-title":"Artificial Intelligence in Ophthalmology: A Comparative Analysis of GPT-3.5, GPT-4, and Human Expertise in Answering StatPearls Questions","volume":"15","author":"Moshirfar","year":"2023","journal-title":"Cureus"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"86","DOI":"10.3928\/01913913-20231005-02","article-title":"Assessment of the Responses of the Artificial Intelligence-based Chatbot ChatGPT-4 to Frequently Asked Questions About Amblyopia and Childhood Myopia","volume":"61","author":"Nikdel","year":"2024","journal-title":"J. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ong, J., Kedia, N., Harihar, S., Vupparaboina, S.C., Singh, S.R., Venkatesh, R., Vupparaboina, K., Bollepalli, S.C., and Chhablani, J. (2023). Applying large language model artificial intelligence for retina International Classification of Diseases (ICD) coding. J. Med. Artif. Intell., 6, Available online: https:\/\/jmai.amegroups.org\/article\/view\/8198\/html.","DOI":"10.21037\/jmai-23-106"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.jfo.2023.05.006","article-title":"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","volume":"46","author":"Panthier","year":"2023","journal-title":"J. Fr. Ophtalmol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1097\/IAE.0000000000004044","article-title":"The Ability of Artificial Intelligence Chatbots Chatgpt and Google Bard to Accurately Convey Preoperative Information for Patients Undergoing Ophthalmic Surgeries","volume":"44","author":"Patil","year":"2024","journal-title":"Retina"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.1007\/s40123-023-00800-2","article-title":"Artificial Intelligence-Based ChatGPT Responses for Patient Questions on Optic Disc Drusen","volume":"12","author":"Potapenko","year":"2023","journal-title":"Ophthalmol. Ther."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108163","DOI":"10.1016\/j.isci.2023.108163","article-title":"Popular large language model chatbots\u2019 accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries","volume":"26","author":"Pushpanathan","year":"2023","journal-title":"iScience"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.2147\/OPTH.S435052","article-title":"The Utility of ChatGPT in Diabetic Retinopathy Risk Assessment: A Comparative Study with Clinical Diagnosis","volume":"17","author":"Raghu","year":"2023","journal-title":"Clin. Ophthalmol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1080\/09273948.2023.2266730","article-title":"Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology","volume":"32","author":"Sen","year":"2024","journal-title":"Ocul. Immunol. Inflamm."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1080\/09273948.2023.2253471","article-title":"Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis","volume":"32","author":"Wei","year":"2024","journal-title":"Ocul. Immunol. Inflamm."},{"key":"ref_51","first-page":"e49903","article-title":"Performance of ChatGPT in Board Examinations for Specialists in the Japanese Ophthalmology Society","volume":"15","author":"Sakai","year":"2023","journal-title":"Cureus"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4905","DOI":"10.1007\/s10792-023-02893-x","article-title":"A comparative study on the knowledge levels of artificial intelligence programs in diagnosing ophthalmic pathologies and intraocular tumors evaluated their superiority and potential utility","volume":"43","author":"Sensoy","year":"2023","journal-title":"Int. Ophthalmol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1007\/s00417-023-06363-z","article-title":"Diagnostic capabilities of ChatGPT in ophthalmology","volume":"262","author":"Shemer","year":"2024","journal-title":"Graefes Arch. Clin. Exp. Ophthalmol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.jsurg.2023.11.019","article-title":"Development and Evaluation of Aeyeconsult: A Novel Ophthalmology Chatbot Leveraging Verified Textbook Knowledge and GPT-4","volume":"81","author":"Singer","year":"2024","journal-title":"J. Surg. Educ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/08820538.2023.2209166","article-title":"ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and Operative Notes","volume":"38","author":"Singh","year":"2023","journal-title":"Semin. Ophthalmol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100485","DOI":"10.1016\/j.xops.2024.100485","article-title":"A Comparative Study of Responses to Retina Questions from Either Experts, Expert-Edited Large Language Models, or Expert-Edited Large Language Models Alone","volume":"4","author":"Tailor","year":"2024","journal-title":"Ophthalmol. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Taloni, A., Borselli, M., Scarsi, V., Rossi, C., Coco, G., Scorcia, V., and Giannaccare, G. (2023). Comparative performance of humans versus GPT-4.0 and GPT-3.5 in the self-assessment program of American Academy of Ophthalmology. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45837-2"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1097\/WNO.0000000000002074","article-title":"Utility of ChatGPT for Automated Creation of Patient Education Handouts: An Application in Neuro-Ophthalmology","volume":"44","author":"Tao","year":"2024","journal-title":"J. Neuroophthalmol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e184","DOI":"10.1055\/s-0043-1774399","article-title":"Improved Performance of ChatGPT-4 on the OKAP Examination: A Comparative Study with ChatGPT-3.5","volume":"15","author":"Teebagy","year":"2023","journal-title":"J. Acad. Ophthalmol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e49324","DOI":"10.2196\/49324","article-title":"Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study","volume":"25","author":"Wilhelm","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wu, G., Lee, D.A., Zhao, W., Wong, A., and Sidhu, S. (2023). ChatGPT: Is it good for our glaucoma patients?. Front. Ophthalmol., 3.","DOI":"10.3389\/fopht.2023.1260415"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1080\/08164622.2023.2298812","article-title":"Talking technology: Exploring chatbots as a tool for cataract patient education","volume":"108","year":"2025","journal-title":"Clin. Exp. Optom."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zandi, R., Fahey, J.D., Drakopoulos, M., Bryan, J.M., Dong, S., Bryar, P.J., Bidwell, A.E., Bowen, R.C., Lavine, J.A., and Mirza, R.G. (2024). Exploring Diagnostic Precision and Triage Proficiency: A Comparative Study of GPT-4 and Bard in Addressing Common Ophthalmic Complaints. Bioengineering, 11.","DOI":"10.3390\/bioengineering11020120"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1034-x","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1038\/s42256-023-00652-2","article-title":"Federated benchmarking of medical artificial intelligence with MedPerf","volume":"5","author":"Karargyris","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_66","unstructured":"European Commission: Directorate-General for Communications Networks, Content and Technology (2019). Ethics Guidelines for Trustworthy AI, Publications Office."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Dave, T., Athaluri, S.A., and Singh, S. (2023). ChatGPT in medicine: An overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1169595"},{"key":"ref_68","unstructured":"Dam, S.K., Hong, C.S., Qiao, Y., and Zhang, C. (2024). A Complete Survey on LLM-based AI Chatbots. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"29","DOI":"10.21037\/jmai-23-94","article-title":"GPT-4 and medical image analysis: Strengths, weaknesses and future directions","volume":"6","author":"Waisberg","year":"2023","journal-title":"J. Med. Artif. Intell."},{"key":"ref_70","unstructured":"Eberhard, D.M., Simons, G.F., and Fennig, C.D. (2025). Ethnologue: Languages of the World, Ethnologue. [28th ed.]."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.joco.2018.09.001","article-title":"Value of medical history in ophthalmology: A study of diagnostic accuracy","volume":"30","author":"Wang","year":"2018","journal-title":"J. Curr. Ophthalmol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"101304","DOI":"10.1016\/j.imu.2023.101304","article-title":"Evaluating large language models for use in healthcare: A framework for translational value assessment","volume":"41","author":"Reddy","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Park, Y.-J., Pillai, A., Deng, J., Guo, E., Gupta, M., Paget, M., and Naugler, C. (2024). Assessing the research landscape and clinical utility of large language models: A scoping review. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02459-6"},{"key":"ref_74","unstructured":"Liu, F., Li, Z., Zhou, H., Yin, Q., Yang, J., Tang, X., Luo, C., Zeng, M., Jiang, H., and Gao, Y. (2024). Large Language Models in the Clinic: A Comprehensive Benchmark. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mohammadi, I., Firouzabadi, S.R., Kohandel Gargari, O., and Habibi, G. (2025). Standardized Assessment Framework for Evaluations of Large Language Models in Medicine (SAFE-LLM). Preprints, 2025010471.","DOI":"10.20944\/preprints202501.0471.v1"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1038\/s41597-024-04159-2","article-title":"MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities","volume":"11","author":"Aydin","year":"2024","journal-title":"Sci. Data"},{"key":"ref_77","unstructured":"Han, T., Kumar, A., Agarwal, C., and Lakkaraju, H. (2024). MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. arXiv."},{"key":"ref_78","unstructured":"(2025, February 10). Privacy Policy. Available online: https:\/\/openai.com\/policies\/row-privacy-policy."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","article-title":"Ethical and regulatory challenges of large language models in medicine","volume":"6","author":"Ong","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e52935","DOI":"10.2196\/52935","article-title":"Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study","volume":"26","author":"Mugaanyi","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Patil, R., Heston, T.F., and Bhuse, V. (2024). Prompt Engineering in Healthcare. Electronics, 13.","DOI":"10.3390\/electronics13152961"},{"key":"ref_82","first-page":"e234","article-title":"Social Determinants, Health Literacy, and Disparities: Intersections and Controversies","volume":"5","author":"Schillinger","year":"2021","journal-title":"Health Lit. Res. Pract."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Al Ghadban, Y., Lu, H., Adavi, U., Sharma, A., Gara, S., Das, N., Kumar, B., John, R., Devarsetty, P., and Hirst, J.E. (2023). Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation. medRxiv.","DOI":"10.1101\/2023.12.15.23300009"},{"key":"ref_84","unstructured":"Wang, X., Chen, N., Chen, J., Wang, Y., Zhen, G., Zhang, C., Wu, X., Hu, Y., Gao, A., and Wan, X. (2024). Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People. arXiv."},{"key":"ref_85","unstructured":"Huang, Z., Zhu, W., Cheng, G., Li, L., and Yuan, F. (2024). MindMerger: Efficient Boosting LLM Reasoning in non-English Languages. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1186\/s13643-021-01821-3","article-title":"Scoping reviews: Reinforcing and advancing the methodology and application","volume":"10","author":"Peters","year":"2021","journal-title":"Syst. Rev."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Eden, J., Levit, L., Berg, A., and Morton, S. (2011). Finding What Works in Health Care: Standards for Systematic Reviews, National Academies Press.","DOI":"10.17226\/13059"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/1748-5908-5-69","article-title":"Scoping studies: Advancing the methodology","volume":"5","author":"Levac","year":"2010","journal-title":"Implement. Sci."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:47:49Z","timestamp":1760032069000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":88,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bdcc9060151"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9060151","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]}}}