RadioRAG: Online Retrieval–Augmented Generation for Radiology Question Answering

作者：Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno K. Bressem, Robert Malte Siepmann, Lisa Adams, Dyke Ferber, Christiane Katharina Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn · 发表于：Radiology Artificial Intelligence · 年份：2025 · DOI：10.1148/ryai.240476 · 被引用次数：27 · 研究领域：Topic Modeling、Computational and Text Analysis Methods、Natural Language Processing Techniques

RadioRAG uses online retrieval–augmented generation to potentially improve the accuracy and factuality of large language model–generated responses to case-based radiology questions by incorporating real-time data from Radiopaedia.