Agentic large language models improve retrieval-based radiology question answering

• URL: http://arxiv.org/abs/2508.00743v2
• Date: Sun, 24 Aug 2025 14:26:30 GMT
• Title: Agentic large language models improve retrieval-based radiology question answering
• Authors: Sebastian Wind, Jeta Sopa, Daniel Truhn, Mahshad Lotfinia, Tri-Thien Nguyen, Keno Bressem, Lisa Adams, Mirabela Rusu, Harald Köstler, Gerhard Wellein, Andreas Maier, Soroosh Tayebi Arasteh,
• Abstract summary: We propose an agentic RAG framework enabling large language models (LLMs) to autonomously decompose radiology questions.<n>LLMs iteratively retrieve targeted clinical evidence from Radiopaedia.org, and dynamically synthesize evidence-based responses.<n>Agentic retrieval significantly improved mean diagnostic accuracy over zero-shot prompting and conventional online RAG.
• Score: 4.208637377704778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-augmented generation (RAG) systems for radiology question answering (QA) typically rely on single-step retrieval, limiting their ability to handle complex clinical reasoning tasks. Here we propose an agentic RAG framework enabling LLMs to autonomously decompose radiology questions, iteratively retrieve targeted clinical evidence from Radiopaedia.org, and dynamically synthesize evidence-based responses. We evaluated 25 LLMs spanning diverse architectures, parameter scales (0.5B to >670B), and training paradigms (general-purpose, reasoning-optimized, clinically fine-tuned), using 104 expert-curated radiology questions from previously established RSNA-RadioQA and ExtendedQA datasets. To assess generalizability, we additionally tested on an unseen internal dataset of 65 real-world radiology board examination questions. Agentic retrieval significantly improved mean diagnostic accuracy over zero-shot prompting and conventional online RAG. The greatest gains occurred in small-scale models, while very large models (>200B parameters) demonstrated minimal changes (<2% improvement). Additionally, agentic retrieval reduced hallucinations (mean 9.4%) and retrieved clinically relevant context in 46% of cases, substantially aiding factual grounding. Even clinically fine-tuned models showed gains from agentic retrieval (e.g., MedGemma-27B), indicating that retrieval remains beneficial despite embedded domain knowledge. These results highlight the potential of agentic frameworks to enhance factuality and diagnostic accuracy in radiology QA, warranting future studies to validate their clinical utility. All datasets, code, and the full agentic framework are publicly available to support open research and clinical translation.

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