Multi-step retrieval and reasoning improves radiology question answering with large language models

Abstract

Large language models (LLMs) show promise for radiology decision support, yet conventional retrieval-augmented generation (RAG) relies on single-step retrieval and struggles with complex reasoning. We introduce radiology Retrieval and Reasoning (RaR), a multi-step retrieval framework that iteratively summarizes clinical questions, retrieves evidence, and synthesizes answers. We evaluated 25 LLMs spanning general-purpose, reasoning-optimized, and clinically fine-tuned models (0.5B → 670B parameters) on 104 expert-curated radiology questions and an independent set of 65 real radiology board-exam questions. RaR significantly improved mean diagnostic accuracy versus zero-shot prompting (75% vs. 67%; P = 1.1 × 10^-7) and conventional online RAG (75% vs. 69%; P = 1.9 × 10^-6). Gains were largest in mid-sized and small models (e.g., Mistral Large: 72% → 81%), while very large models showed minimal change. RaR reduced hallucinations and provided clinically relevant evidence in 46% of cases, improving factual grounding. These results show that multi-step retrieval enhances diagnostic reliability, especially in deployable mid-sized LLMs. Code, datasets, and RaR are publicly available.

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