ReviewFrontiers in medicine2023
Exploring large language model for next generation of artificial intelligence in ophthalmology.
Review in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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Who cites it
25 citing papers in PubMed.
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- EyeRAG: graph retrieval-augmented generation for safe and accurate clinical dialogue in ophthalmology.NPJ digital medicine · 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
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique.Beyoglu eye journal · 2026Article
- Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma.Frontiers in medicine · 2026Article
- Leveraging ChatGPT for Report Error Audit: An Accuracy-Driven and Cost-Efficient Solution for Ophthalmic Imaging Reports.Ophthalmology and therapy · 2025Article
- Enhancing diabetic retinopathy query responses: assessing large language model in ophthalmology.The British journal of ophthalmology · 2025Article
- ChatGPT-4 for addressing patient-centred frequently asked questions in age-related macular degeneration clinical practice.Eye (London, England) · 2025Article
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- Evaluating the Efficacy of Large Language Models in Guiding Treatment Decisions for Pediatric Refractive Error.Ophthalmology and therapy · 2025Article
- Article
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Real-world evidence and the future of personalized medicine: a global perspective on data, ethics, and equity.Frontiers in health services · 2025Review
- Assessment of Large Language Models in Cataract Care Information Provision: A Quantitative Comparison.Ophthalmology and therapy · 2025Article
- Article
- Assessing large language models' accuracy in providing patient support for choroidal melanoma.Eye (London, England) · 2024Article
- A Performance Evaluation of Large Language Models in Keratoconus: A Comparative Study of ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs' potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs' competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs' promising role in shaping AI's future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices.
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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.