ArticleNPJ digital medicine2025
A multimodal visual-language foundation model for computational ophthalmology.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
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Who cites it
36 citing papers in PubMed.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- From generalization to precision: A large domain-specific pretrained model for specialized medical tasks.Cell reports. Medicine · 2026Article
- An autonomous multimodal AI agent for evidence-grounded ophthalmic diagnosis.Cell reports. Medicine · 2026Article
- Review
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- FetalCLIP: a visual-language foundation model for fetal ultrasound image analysis.NPJ digital medicine · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
- Fundus to fluorescein angiography video generation as a retinal generative foundation model.NPJ digital medicine · 2026Article
- MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.Journal of imaging · 2026Article
- A gated task-attentive multi-task network for unified retinal image analysis.Scientific reports · 2026Article
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images.medRxiv : the preprint server for health sciences · 2026Article
- Angiography-free diagnosis of retinal diseases via interpretable multi-modal learning.NPJ digital medicine · 2026Article
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Early detection of eye diseases is vital for preventing vision loss. Existing ophthalmic artificial intelligence models focus on single modalities, overlooking multi-view information and struggling with rare diseases due to long-tail distributions. We propose EyeCLIP, a multimodal visual-language foundation model trained on 2.77 million ophthalmology images from 11 modalities with partial clinical text. Our novel pretraining strategy combines self-supervised reconstruction, multimodal image contrastive learning, and image-text contrastive learning to capture shared representations across modalities. EyeCLIP demonstrates robust performance across 14 benchmark datasets, excelling in disease classification, visual question answering, and cross-modal retrieval. It also exhibits strong few-shot and zero-shot capabilities, enabling accurate predictions in real-world, long-tail scenarios. EyeCLIP offers significant potential for detecting both ocular and systemic diseases, and bridging gaps in real-world clinical applications.
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.