Capability of GPT-4V(ision) in Japanese National Medical Licensing Examination

Abstract

Abstract Background Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images. Objective To evaluate the capability of GPT-4V, a recent multimodal LLM developed by OpenAI, in recognizing images in the medical field by testing its capability to answer questions in the 117th Japanese National Medical Licensing Examination. Methods We focused on 108 questions that had one or more images as part of a question and presented GPT-4V with the same questions under two conditions: 1) with both the question text and associated image(s), and 2) with the question text only. We then compared the difference in accuracy between the two conditions using the exact McNemar’s test. Results Among the 108 questions with images, GPT-4V’s accuracy was 68% when presented with images and 72% when presented without images ( P = .36). Conclusions The additional information from the images did not significantly improve the performance of GPT-4V in the Japanese Medical Licensing Examination.

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