ArticleOphthalmology science2026
Comprehensive Evaluation of ChatGPT's Diagnostic Accuracy on Image-based Ophthalmic Case Interpretations.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
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Authors and funding
3 authors.
Funding
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Abstract
Objective: To evaluate the diagnostic and treatment accuracy of Chat Generative Pre-trained Transformer (GPT-4.o) in ophthalmology, comparing performance when provided with full clinical context versus image-only inputs. Design: Cross-sectional diagnostic accuracy study. Subjects: A total of 261 ophthalmic cases from the University of Iowa's publicly available EyeRounds repository, spanning 6 subspecialties (pediatrics, retina, neuro-ophthalmology, glaucoma, oculoplastics, and cornea/cataract). Methods: Each case was analyzed under 2 conditions: (1) full-context input (clinical narrative and expert-generated image descriptions) and (2) image-only input (raw clinical images without text). Chat Generative Pre-trained Transformer was prompted to identify key signs/symptoms, differential diagnoses, final diagnosis, and treatment recommendations. Outputs were compared to expert-authored case solutions and evaluated for diagnostic accuracy and component match rates. Logistic regression was used to determine which output features predicted correct diagnosis. Main Outcome Measures: Primary outcome: diagnostic accuracy (match to expert diagnosis). Secondary outcomes: percentage match of signs/symptoms, differential diagnoses, and treatment recommendations. Results: Diagnostic accuracy was significantly higher with full-context input (80.1%) than image-only input (54.7%) (χ Conclusions: Chat Generative Pre-trained Transformer demonstrated high diagnostic accuracy when provided with full clinical context but significantly reduced performance with image-only input. These findings highlight the limitations of multimodal artificial intelligence (AI) in interpreting raw ophthalmic images and underscore the importance of integrating structured clinical data to optimize AI-driven diagnostic support. Financial Disclosures: The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.
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