ArticleFrontiers in ophthalmology2024
Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus images.
Article in Frontiers in ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 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
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Exploring AI's Potential in Papilledema Diagnosis to Support Dermatological Treatment Decisions in Rural Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- What will the future role for large language models (LLMs) be in managing patients with glaucoma?Expert review of ophthalmology · 2025Article
- Multiple large language models versus experienced physicians in diagnosing challenging cases with gastrointestinal symptoms.NPJ digital medicine · 2025Article
- Application of large language models in disease diagnosis and treatment.Chinese medical journal · 2025Review
- Assessing the Diagnostic Capabilities of ChatGPT-4 Omni in Grading Diabetic Retinopathy Fundoscopy Using Color Fundus Photographs.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis.Ophthalmology scienceArticle
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3 authors.
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Abstract
Overview: This study evaluates the diagnostic accuracy of a multimodal large language model (LLM), ChatGPT-4, in recognizing glaucoma using color fundus photographs (CFPs) with a benchmark dataset and without prior training or fine tuning. Methods: The publicly accessible Retinal Fundus Glaucoma Challenge "REFUGE" dataset was utilized for analyses. The input data consisted of the entire 400 image testing set. The task involved classifying fundus images into either 'Likely Glaucomatous' or 'Likely Non-Glaucomatous'. We constructed a confusion matrix to visualize the results of predictions from ChatGPT-4, focusing on accuracy of binary classifications (glaucoma vs non-glaucoma). Results: ChatGPT-4 demonstrated an accuracy of 90% with a 95% confidence interval (CI) of 87.06%-92.94%. The sensitivity was found to be 50% (95% CI: 34.51%-65.49%), while the specificity was 94.44% (95% CI: 92.08%-96.81%). The precision was recorded at 50% (95% CI: 34.51%-65.49%), and the F1 Score was 0.50. Conclusion: ChatGPT-4 achieved relatively high diagnostic accuracy without prior fine tuning on CFPs. Considering the scarcity of data in specialized medical fields, including ophthalmology, the use of advanced AI techniques, such as LLMs, might require less data for training compared to other forms of AI with potential savings in time and financial resources. It may also pave the way for the development of innovative tools to support specialized medical care, particularly those dependent on multimodal data for diagnosis and follow-up, irrespective of resource constraints.
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