Large language models and their impact in ophthalmology
Open accessAbstract
The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.
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AI agents continuously evolve through iterative learning from new datasets, enabling them to adapt to emerging medical knowledge and evolving clinical standards.
“Moreover, AI agents continuously evolve through iterative learning from new datasets, enabling them to adapt to emerging medical knowledge and evolving clinical standards.73,74”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [73]
The section on streamlining medical-record documentation (page unavailable) anticipates improvements as LLMs evolve and are trained on more comprehensive datasets. But the capability-limitations section warns that LLMs might lag behind new research and clinical guidelines. The paper does not say AI agents continuously learn from new datasets or thereby keep pace with evolving clinical standards. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThis capability is crucial for creating virtual patients that accurately simulate real patients for educational purposes.
“This capability is crucial for creating VPs that accurately simulate real patients for educational purposes39,40.”
From A large language model digital patient system enhances ophthalmology history taking skills (2025) · cites this paper as [40]
The section “Enhancing Ophthalmic Education with LLMs” (page not provided) says LLMs can role-play real-world interactions and simulate clinical scenarios for training. It does not establish that they create virtual patients that accurately simulate real patients, or that this capability is crucial to doing so. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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The potential of large language models to advance precision oncology
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Latest developments of generative artificial intelligence and applications in ophthalmology
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Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study
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A data-driven risk assessment of cybersecurity challenges posed by generative AI
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Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models
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Self-citationEvaluation and practical application of prompt-driven ChatGPTs for EMR generation
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Self-citationThe evolution of diabetic retinopathy screening
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Self-citationUtilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges
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Self-citationArtificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review
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APPLICATIONS OF MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE IN A REAL-WORLD RETINA CLINIC SETTING
Ghalibafan, S., Gonzalez, D. J. T., CAI, L. Z., Chou, B. G., Panneerselvam, S., Barrett, S. C., Djulbegovic, M. B., & Yannuzzi, N. A. (2024) · Retina · doi:10.1097/iae.0000000000004204 · Cited by 14 · see citing works
Comparative performance of large language models for patient-initiated ophthalmology consultations
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Can OpenAI's New o1 Model Outperform Its Predecessors in Common Eye Care Queries?
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Self-citationChatMyopia: An AI agent for myopia-related consultation in primary eye care settings
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Assessing DeepSeek-R1 for Clinical Decision Support in Multidisciplinary Laboratory Medicine
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Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis
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Self-citationDeepSeek TM and lacrimal drainage disorders: hype or is it performing better than ChatGPT TM ?
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Self-citationGlobal variations in artificial intelligence-generated information on juvenile idiopathic arthritis
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Evaluating the performance of ChatGPT in clinical multidisciplinary treatment: a retrospective study
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Self-citationChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries
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Big data for imaging assessment in glaucoma
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Can ChatGPT-4 perform as a competent physician based on the Chinese critical care examination?
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Self-citationBenchmarking large multimodal models for ophthalmic visual question answering with OphthalWeChat
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Self-citationGlobal perspectives of ophthalmologists on artificial intelligence adoption in clinical practice
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A look at the emerging trends of large language models in ophthalmology
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EVALUATING LARGE LANGUAGE MODELS ON THEIR ACCURACY AND COMPLETENESS
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Evaluating ChatGPT’s Diagnostic Accuracy in Detecting Fundus Images
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Comparative performance analysis of global and chinese-domain large language models for myopia
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Large language model integration in Philippine ophthalmology: early challenges and steps forward
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Shean, R. S., Mallapu, J. K., Shah, T., Rasheed, H. A., Younessi, D. N., Tham, Y. C., Nguyen, V., Bolo, K. A., & Xu, B. Y. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101145 · Cited by 2 · see citing works
Self-citationComparative Evaluation of Deep-Reasoning Large Language Models for Ophthalmic Emergencies
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Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models
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Evaluation of ophthalmic large language models: quantitative vs. qualitative methods
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Self-citationThe potential of large language models in the field of infertility: a systematic review
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Self-citationLarge language models and eye education: A narrative review
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- Authors
- Betzler, B. K., Chen, H., Cheng, C.-Y., Lee, C. S., Ning, G., Song, S. J., Lee, A. Y., Kawasaki, R., van Wijngaarden, P., Grzybowski, A., He, M., Li, D., Ran Ran, A., Ting, D. S. W., Teo, K., Ruamviboonsuk, P., Sivaprasad, S., Chaudhary, V., Tadayoni, R., Wang, X., Cheung, C. Y., Zheng, Y., Wang, Y. X., Tham, Y. C., & Wong, T. Y.
- Affiliations
- National University of Singapore (SG)
- Tsinghua University (CN)
- University of Washington (US)
- Kangbuk Samsung Hospital (KR)
- Osaka University Hospital (JP)
- Foundation for the Development of Ophthalmology (PL)
- Hong Kong Polytechnic University (HK)
- Peking University (CN)
- Rajavithi Hospital (TH)
- NIHR Moorfields Biomedical Research Centre (GB)
- McMaster University (CA)
- Université Paris Cité (FR)
- Beihang University (CN)
- Chinese University of Hong Kong (HK)
- Sun Yat-sen University (CN)
- Beijing Tongren Hospital (CN)
- Singapore National Eye Center (SG)
- Corresponding author
- Tham, Yih Chung
- ORCID
- Betzler, Bjorn Kaijun
- Chen, Haichao
- Cheng, Ching-Yu
- Lee, Cecilia S
- Ning, Guochen
- Song, Su Jeong
- Lee, Aaron Y
- Kawasaki, Ryo
- Grzybowski, Andrzej
- He, Mingguang
- Li, Dawei
- Ting, Daniel Shu Wei
- Teo, Kelvin
- Ruamviboonsuk, Paisan
- Sivaprasad, Sobha
- Chaudhary, Varun
- Tadayoni, Ramin
- Wang, Xiaofei
- Cheung, Carol Y
- Zheng, Yingfeng
- Wang, Ya Xing
- Tham, Yih Chung
- Wong, Tien Yin
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 5
- Issue
- 12
- Pages
- e917-e924
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC11003328
- OpenAlex
- W4388922836
- Cited by
- 161
- References
- 62
- Citations by year
- 2026: 52 · 2025: 60 · 2024: 49
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, other-oa)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
- Funders
- National Medical Research Council (NMRC/MOH/HCSAINV21nov-0001)
- National Key Research and Development Program of China (2022YFC2502800)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, ophthalmology, generative AI, clinical workflow, patient experience, data privacy, ethical considerations, clinical implementation
- MeSH terms
- Artificial Intelligence, Humans, Language, Medicine, Ophthalmology, Privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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