ArticleAnnals of nuclear medicine2025
Role of visual information in multimodal large language model performance: an evaluation using the Japanese nuclear medicine board examination.
Article in Annals of nuclear medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- AI achieves board-level performance on the Japan diagnostic radiology board examination through direct image interpretation.Japanese journal of radiology · 2026Article
- The impact of large language models on collaborative learning processes and outcomes in higher education for English translation.Scientific reports · 2026Article
- Assessing accuracy and legitimacy of multimodal large language models on Japan Diagnostic Radiology Board Examination.Japanese journal of radiology · 2026Article
- Vision-language model performance on the Japanese Nuclear Medicine Board Examination: high accuracy in text but challenges with image interpretation.Annals of nuclear medicine · 2025Article
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6 authors.
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Abstract
objectivesThis study aimed to assess the performance of state-of-the-art multimodal large language models (LLMs), specifically GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, on Japanese Nuclear Medicine Board Examination (JNMBE) questions and to evaluate the influence of visual information on the decision-making process.
methodsThis study utilized 92 questions with images from the JNMBE (2019-2023). The LLMs' responses were assessed under two conditions: providing both text and images and providing only text. Each model answered all questions thrice, and the most frequent answer choice was considered the final answer. The accuracy and agreement rates among the model answers were evaluated using statistical tests.
resultsGPT-4o, Claude 3 Opus, and Gemini 1.5 Pro exhibited no significant differences in terms of accuracy between the text-and-image and text-only conditions. GPT-4o and Claude 3 Opus demonstrated accuracies of 54.3% (95% CI: 44.2%-64.1%) each when provided with both text and images; however, they selected the same options as in the text-only condition for 71.7% of the questions. Gemini 1.5 Pro performed significantly worse than GPT-4o under text and image conditions. The agreement rates among the model answers ranged from weak to moderate.
conclusionThe influence of images on decision-making in nuclear medicine is limited to the latest multimodal LLMs, and their diagnostic ability in this highly specialized field remains insufficient. Improving the utilization of image information and enhancing the answer reproducibility are crucial for the effective application of LLMs in nuclear medicine education and practice. Further advancements in these areas are necessary to harness the potential of LLMs as assistants in nuclear medicine diagnosis.
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