ArticleClinical ophthalmology (Auckland, N.Z.)2025
Image Recognition Performance of GPT-4V(ision) and GPT-4o in Ophthalmology: Use of Images in Clinical Questions.
Article in Clinical ophthalmology (Auckland, N.Z.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Abstraction-dependent diagnostic performance of a multimodal foundation model in oral epithelial dysplasia.Odontology · 2026Article
- A comparison of GPT-4V's capability in optical coherence tomography images of age-related macular degeneration with expert assessments.BMC 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
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Assessment of large language models in musculoskeletal radiological anatomy: A comparative study with radiologists.Joint diseases and related surgery · 2026Article
- Review
- Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study.JMIR formative research · 2024Article
- Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis.Ophthalmology scienceArticle
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Authors and funding
5 authors.
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Abstract
Purpose: To compare the diagnostic accuracy of Generative Pre-trained Transformer with Vision (GPT)-4, GPT-4 with Vision (GPT-4V), and GPT-4o for clinical questions in ophthalmology. Patients and Methods: The questions were collected from the "Diagnosis This" section on the American Academy of Ophthalmology website. We tested 580 questions and presented ChatGPT with the same questions under two conditions: 1) multimodal model, incorporating both the question text and associated images, and 2) text-only model. We then compared the difference in accuracy using McNemar tests among multimodal (GPT-4o and GPT-4V) and text-only (GPT-4V) models. The percentage of general correct answers was also collected from the website. Results: Multimodal GPT-4o performed the best accuracy (77.1%), followed by multimodal GPT-4V (71.0%), and then text-only GPT-4V (68.7%); (P values < 0.001, 0.012, and 0.001, respectively). All GPT-4 models showed higher accuracy than the general correct answers on the website (64.6%). Conclusion: The addition of information from images enhances the performance of GPT-4V in diagnosing clinical questions in ophthalmology. This suggests that integrating multimodal data could be crucial in developing more effective and reliable diagnostic tools in medical fields.
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