ArticleAmerican journal of ophthalmology2024
Predicting Glaucoma Before Onset Using a Large Language Model Chatbot.
Article in American journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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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
27 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
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study.International ophthalmology · 2026Article
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Comparative performance of LLMs and machine learning in predicting complications after percutaneous kyphoplasty for osteoporotic vertebral compression fractures.NPJ digital medicine · 2026Article
- Improving support and self-management of ophthalmic patients using an artificial intelligence health coach.Indian journal of ophthalmology · 2026Article
- ChatGPT-Assisted Glaucoma Diagnosis: A Health-Equitable Multi-Ancestry Analysis Using Visual Field and Optical Coherence Tomography Data.American journal of ophthalmology · 2026Article
- Applications of Large Language Models in Glaucoma: A Scoping Review.Vision (Basel, Switzerland) · 2026Review
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Table-based language models for ophthalmology assessment in the emergency department.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma.Frontiers in medicine · 2026Article
- Risk-centered benchmarking of large language models for AI-enabled counseling in chronic autoimmune thyroid eye disease.Frontiers in cell and developmental biology · 2026Article
- Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT.Translational vision science & technology · 2025Article
- Large language models in ophthalmology: a scoping review on their utility for clinicians, researchers, patients, and educators.Eye (London, England) · 2025Article
- Ophthalmological Question Answering and Reasoning Using OpenAI o1 vs Other Large Language Models.JAMA ophthalmology · 2025Article
- GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia.Ophthalmology and therapy · 2025Article
- What will the future role for large language models (LLMs) be in managing patients with glaucoma?Expert review of ophthalmology · 2025Article
- Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive Care IV (MIMIC-IV) Database.Journal of medical Internet research · 2025Article
- Evaluating the performance of ChatGPT in patient consultation and image-based preliminary diagnosis in thyroid eye disease.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo investigate the capability of ChatGPT for forecasting the conversion from ocular hypertension (OHT) to glaucoma based on the Ocular Hypertension Treatment Study (OHTS).
designRetrospective case-control study.
participantsA total of 3008 eyes of 1504 subjects from the OHTS were included in the study.
methodsWe selected demographic, clinical, ocular, optic nerve head, and visual field (VF) parameters 1 year before glaucoma development from the OHTS participants. Subsequently, we developed queries by converting tabular parameters into textual format based on both eyes of all participants. We used the ChatGPT application program interface (API) to automatically perform ChatGPT prompting for all subjects. We then investigated whether ChatGPT can accurately forecast conversion from OHT to glaucoma based on various objective metrics.
main outcome measureAccuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and weighted F1 score.
resultsChatGPT4.0 demonstrated an accuracy of 75%, AUC of 0.67, sensitivity of 56%, specificity of 78%, and weighted F1 score of 0.77 in predicting conversion to glaucoma 1 year before onset. ChatGPT3.5 provided an accuracy of 61%, AUC of 0.62, sensitivity of 64%, specificity of 59%, and weighted F1 score of 0.63 in predicting conversion to glaucoma 1 year before onset.
conclusionsThe performance of ChatGPT4.0 in forecasting development of glaucoma 1 year before onset was reasonable. The overall performance of ChatGPT4.0 was consistently higher than ChatGPT3.5. Large language models (LLMs) hold great promise for augmenting glaucoma research capabilities and enhancing clinical care. Future efforts in creating ophthalmology-specific LLMs that leverage multimodal data in combination with active learning may lead to more useful integration with clinical practice and deserve further investigations.
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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.