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Abstract #124911 Published in IGR 25-2
Image Recognition Performance of GPT-4V(ision) and GPT-4o in Ophthalmology: Use of Images in Clinical Questions
Tomita K; Nishida T; Kitaguchi Y; Kitazawa K; Miyake MClinical Ophthalmology 2025; 19: 1557-1564
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.
Department of Ophthalmology, Kawasaki Medical School, Okayama, Japan.
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