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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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“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”
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“This capability is crucial for creating VPs that accurately simulate real patients for educational purposes39,40.”
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Metadata
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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