Retrieval-Augmented Generation with Large Language Models in Radiology: From Theory to Practice

Abstract

Large language models (LLMs) hold substantial promise in addressing the growing workload in radiology, but recent studies also reveal limitations, such as hallucinations and opacity in sources for LLM responses. Retrieval-augmented generation (RAG)-based LLMs offer a promising approach to streamline radiology workflows by integrating reliable, verifiable, and customizable information. Ongoing refinement is critical in order to enable RAG models to manage large amounts of input data and to engage in complex multiagent dialogues. This report provides an overview of recent advances in LLM architecture, including few-shot and zero-shot learning, RAG integration, multistep reasoning, and agentic RAG, and identifies future research directions. Exemplary cases demonstrate the practical application of these techniques in radiology practice. <b>Keywords:</b> Artificial Intelligence, Deep Learning, Natural Language Processing, Tomography, x-Ray © RSNA, 2025.

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