RadioRAG: Online Retrieval–Augmented Generation for Radiology Question Answering

Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno K. Bressem, Robert Siepmann, Dyke Ferber, Christiane Kühl, Sven Nebelung

Radiology Artificial Intelligence · 2025 · 20 citations · 25 references

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Concepts

Abstract

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.

References

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