ArticleOphthalmology and therapy2023
The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports.
Article in Ophthalmology and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 5 of them syntheses that pooled it.
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Who cites it
69 citing papers in PubMed, 5 syntheses or guidelines pooled it, 116 citations in OpenAlex.
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study.International ophthalmology · 2026Article
- Comprehensive Evaluation of ChatGPT's Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations.Ophthalmology science · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Comparative performance of large language models in answering cornea and cataract surgery questions for resident training.BMC ophthalmology · 2026Article
- Enhancing trustworthiness of Arabic online health information quality evaluation using an enhanced BERT architecture with PCA and ICA feature weighting.Scientific reports · 2026Article
- Multimodal Diagnostic Accuracy of GPT-4.o, Claude 3.7, and Gemini 2.5 on Real-World Retina Cases.Journal of vitreoretinal diseases · 2026Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Assessing retina-specific ophthalmic counseling generated by an early public large language model across different levels of clinical urgency.Frontiers in digital health · 2026Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- Effectiveness and satisfaction of fully self-service fundus disease screening among middle-aged individuals: a cross-sectional study.BMJ open ophthalmology · 2025Article
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Evaluating Large Language Models in Ptosis-Related inquiries: A Cross-Lingual Study.Translational vision science & technology · 2025Article
- Enhancing Diagnostic Accuracy of Ophthalmological Conditions With Complex Prompts in GPT-4: Comparative Analysis of Global and Low- and Middle-Income Country (LMIC)-Specific Pathologies.JMIR formative research · 2025Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
introductionThe purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees.
methodsWe selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements.
resultsThe provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively.
conclusionsThe accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.
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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.