ArticleiScience2025
EYE-Llama, an in-domain large language model for ophthalmology.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.Ophthalmology science · 2026Article
- A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.Cell reports. Medicine · 2026Article
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Comparison of Emotional Content in Text Responses From Physicians and AI Chatbots to Patient Health Queries: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning.Ophthalmology science · 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
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.Communications medicine · 2025Article
- Streamlining Ophthalmic Documentation With Anonymized, Fine-Tuned Language Models: Feasibility Study.Interactive journal of medical research · 2025Article
- Review
- Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model.ArXiv · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
- Re-identification of patients from imaging features extracted by foundation models.NPJ digital medicine · 2025Article
- OphthaBERT: Automated Glaucoma Diagnosis from Clinical Notes.medRxiv : the preprint server for health sciences · 2025Article
- Distributed training of foundation models for ophthalmic diagnosis.Communications engineering · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease.Frontiers in endocrinology · 2025Article
- Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model.ArXiv · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Training large language models (LLMs) on domain-specific data enhances their performance, yielding more accurate and reliable question-answering (Q&A) systems that support clinical decision-making and patient education. We present EYE-Llama, pretrained on ophthalmology-focused datasets, including PubMed abstracts, textbooks, and online articles, and fine-tuned on diverse Q&A pairs. We evaluated EYE-Llama against Llama 2, Llama 3, Meditron, ChatDoctor, ChatGPT, and several other LLMs. Using BERT (Bidirectional Encoder Representations from Transformers) score, BART (Bidirectional and Auto-Regressive Transformer) score, and BLEU (Bilingual Evaluation Understudy) metrics, EYE-Llama achieved superior scores. On the MedMCQA benchmark, it outperformed Llama 2, Meditron, and ChatDoctor. On PubMedQA, it achieved 0.96 accuracy, surpassing all models tested. These results demonstrate that domain-specific pretraining and fine-tuning significantly improve medical Q&A performance and underscore the value of specialized models such as EYE-Llama.
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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.