ArticleAmerican journal of ophthalmology2026
ChatGPT-Assisted Glaucoma Diagnosis: A Health-Equitable Multi-Ancestry Analysis Using Visual Field and Optical Coherence Tomography Data.
Article in American journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 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
- Patient education for neuromyelitis optica spectrum disorder using large language models: combining expert assessment and real-world patient interaction.Frontiers in physiology · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Caregiver burden and anticipatory grief among family caregivers of patients with advanced cancer in a palliative context: chain mediating role of social support and positive coping.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
purposeEarly glaucoma detection is challenging due to variable ocular anatomy, non-glaucomatous optic neuropathy impacting optical coherence tomography (OCT) results, and the subjective nature of visual field (VF) tests. Multimodal large language models may overcome these challenges to provide equitable and accurate screening diagnoses across ancestries and glaucoma genetic predispositions. We evaluated ChatGPT o1 Pro's accuracy in identifying glaucoma using circumpapillary retinal nerve fiber layer (RNFL) OCT and VF data, and its consistency across ancestries and glaucoma polygenic risk scores (PRS).
designCross-sectional diagnostic accuracy study. SETTINGS AND
participantsWe enrolled 204 participants from the Mount Sinai BioMe Biobank for a comprehensive ophthalmic examination from November 2022 to March 2025. This cross-sectional diagnostic accuracy study included 38% European (EUR) and 62% non-European (non-EUR) participants stratified by low/intermediate (n = 107) and high-risk glaucoma PRS (n = 97). Two glaucoma specialists masked to PRS status provided a consensus reference diagnosis. ChatGPT received only de-identified VFs and OCT-RNFL numerical outputs to determine glaucoma status. Performance metrics were compared with the reference diagnosis. Subgroup comparisons by ancestry (EUR versus non-EUR) and PRS (high versus low/intermediate) were conducted. We used logistic regression models to assess the impacts of ancestry, PRS and ocular parameters on classification accuracy.
main outcome measuresChatGPT o1 Pro's diagnostic performance in detecting glaucoma compared to consensus specialist diagnoses, stratified by ancestry and genetic risk.
resultsChatGPT o1 Pro exhibited 96.0% sensitivity (95% confidence interval (CI): 88.3%-100%), 83.7% specificity (95% CI: 78.3%-89.1%), 85.2% accuracy (95% CI: 80.3%-90.1%), an area under the receiver operator curve (AUC) of 0.899, a positive predictive value (PPV) of 45.3% (95% CI: 31.9%-58.7%), and a negative predictive value (NPV) of 99.3% (95% CI: 98.0%-100%); κ for agreement with the consensus reference was 0.538. No significant differences were observed between EUR and non-EUR subgroups (AUC: 0.894 vs 0.906, P = .79; accuracy: 88.3% vs 83.3%, P = .44) or high and low/intermediate-PRS subgroups (AUC: 0.889 vs 0.922, P = .45; accuracy: 85.4% vs 85.0%, P = .50). Global RNFL was the only determinant of reference disease classification (OR = 1.1 per micron, P < .001).
conclusionChatGPT o1 Pro diagnosed glaucoma similarly to specialists using only VF and OCT data. The model performance was similar across ancestral groups and genetic predispositions to glaucoma.
Indexed as
Identifiers
What OpenQuestion holds
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.