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Image Recognition Performance of GPT-4V(ision) and GPT-4o in Ophthalmology: Use of Images in Clinical Questions.

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Název: Image Recognition Performance of GPT-4V(ision) and GPT-4o in Ophthalmology: Use of Images in Clinical Questions.
Autoři: Tomita, Kosei, Nishida, Takashi, Kitaguchi, Yoshiyuki, Kitazawa, Koji, Miyake, Masahiro
Zdroj: Clinical Ophthalmology; May2025, Vol. 19, p1557-1564, 8p
Témata: GENERATIVE pre-trained transformers, LANGUAGE models, CHATGPT, IMAGE recognition (Computer vision), DIAGNOSTIC imaging
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Biomedical Index
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Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:11775467
DOI:10.2147/OPTH.S494480