ReviewThe British journal of ophthalmology2024
Foundation models in ophthalmology.
Review in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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
24 citing papers in PubMed.
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Review
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Deep learning strategies for estimating retinal thickness from fundus images: a comparative study with multi-device data.Scientific reports · 2026Article
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
- FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images.Translational vision science & technology · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Performance of GPT-5 Frontier Models in Ophthalmology Question Answering.Ophthalmology science · 2026Article
- Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background.Frontiers in medicine · 2026Article
- Can Multimodal Large Language Models Diagnose Diabetic Retinopathy from Fundus Photos? A Quantitative Evaluation.Ophthalmology science · 2026Article
- Global perspectives of ophthalmologists on artificial intelligence adoption in clinical practice.International journal of retina and vitreous · 2025Article
- Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT.Translational vision science & technology · 2025Article
- DeepSeek-R1 vs OpenAI o1 for Ophthalmic Diagnoses and Management Plans.JAMA ophthalmology · 2025Article
- Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging.Scientific reports · 2025Article
- Selecting the Right AI Algorithm for the Job: A Guide for Navigating the AI Jungle in Ophthalmology.Ophthalmology and therapy · 2025Article
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- A multimodal visual-language foundation model for computational ophthalmology.NPJ digital medicine · 2025Article
- Multimodal Performance of GPT-4 in Complex Ophthalmology Cases.Journal of personalized medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice. We begin by outlining the key concepts and technological advances which have enabled the development of these models, providing an overview of novel training approaches and modern AI architectures. Next, we summarise existing literature on the topic of foundation models in ophthalmology, encompassing progress in vision foundation models, large language models and large multimodal models. Finally, we outline major challenges relating to privacy, bias and clinical validation, and propose key steps forward to maximise the benefit of this powerful technology.
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.