Evaluation of a retrieval-augmented generation system using a Japanese Institutional Nuclear Medicine Manual and large language model-automated scoring

Recent advances in large language models (LLMs) enable domain-specific question answering using external knowledge. However, addressing information that is not included in training data remains a challenge, particularly in nuclear medicine, where examination protocols are frequently updated and vary...

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Veröffentlicht in:	Radiological physics and technology Jg. 18; H. 3; S. 861 - 876
Hauptverfasser:	Fukui, Yusuke, Kawata, Yuhei, Kobashi, Kazumasa, Nagatani, Yukihiro, Iguchi, Harumi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.09.2025 Springer Nature B.V
Schlagworte:	Application programming interface Artificial intelligence Automation Certification Datasets Documents Drug dosages Humans Imaging Information Storage and Retrieval - methods Japan Keywords Kidneys Knowledge Language Large Language Models Manuals Medical and Radiation Physics Medicine Medicine & Public Health Nuclear Medicine Performance evaluation Questions Radiology Radiotherapy Research Article Retrieval Scintigraphy Japan Nuclear medicine Medical question answering Large language models (LLMs) Retrieval-augmented generation (RAG)
ISSN:	1865-0333, 1865-0341, 1865-0341
Online-Zugang:	Volltext
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