ReviewThe Lancet. Digital health2023
Large language models and their impact in ophthalmology.
Review in The Lancet. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
92 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The potential of large language models in the field of infertility: a systematic review.Journal of assisted reproduction and genetics · 2025Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Evaluating the Performance of ChatGPT on Board-Style Examination Questions in Ophthalmology: A Meta-Analysis.Journal of medical systems · 2025Pooled it
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Application of Narrative and AI-Assisted Follow-Up After Voluntary Medical Male Circumcision: Multicenter, Double-Blind, Prospective, Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- TeleGlaukos: A Clinician-Led Adaptive Telemonitoring Platform for Hybrid Glaucoma Care.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.Ophthalmology science · 2026Article
- Advertising and large language models: a new frontier influencing medical practice.Eye (London, England) · 2026Article
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Comparative Evaluation of Deep-Reasoning Large Language Models for Ophthalmic Emergencies.Ophthalmology science · 2026Article
- Comparative Performance of Gemini 3 Pro and GPT-5 Family Models on Ophthalmology Board-Style Questions.Ophthalmology science · 2026Article
- Evaluating and enhancing the performance of large language models in thyroid eye disease through customization and Chain-of-Thought strategies.Scientific reports · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Evaluation of large language models in emergency medicine scenarios: a comparative analysis of ChatGPT-4o, ChatGPT-o3mini, Gemini 2.0-pro, and DeepSeek-R1.BMC emergency medicine · 2026Article
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.