ArticleJournal of radiation research2026
Evaluating the capability of large language models in radiotherapy through professional certification examinations in Japan.
Article in Journal of radiation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Article
- Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy.Journal of radiation research · 2026Article
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13 authors.
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Abstract
Large language models (LLMs), such as ChatGPT and Grok, have rapidly advanced in natural language understanding and are increasingly being applied to specialized fields, including medicine. In this study, we evaluated the domain-specific knowledge of LLMs in radiotherapy by assessing their performance on three certification examinations in Japan: the Japanese Medical Physicist Examination, the Japanese Board Examination for Radiologists and the Japanese Board Examination for Radiation Oncologists. We assessed five LLMs-ChatGPT-5, ChatGPT-5 Pro, Grok 4, Grok 4 heavy and Gemini 2.5 Pro-by inputting all multiple-choice questions from these exams into each model and recording their responses. The AI-generated answers were compared with reference answers determined by experienced medical physicists and radiation oncologists. The results demonstrated average accuracies of 84.7 ± 2.0% (ChatGPT-5), 94.7 ± 2.1% (ChatGPT-5 Pro), 78.4 ± 1.2% (Grok 4), 81.6 ± 2.2% (Grok 4 heavy) and 88.9 ± 1.2% (Gemini 2.5 Pro). All models achieved over 75% accuracy, with ChatGPT-5 Pro consistently outperforming others, attaining an average accuracy exceeding 90% across all examinations. These findings highlight the strong potential of advanced LLMs, particularly ChatGPT-5 Pro, for future integration into radiotherapy-related applications such as automated contouring and treatment planning support.
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