ArticleNPJ digital medicine2025
Multi-step retrieval and reasoning improves radiology question answering with large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Retrieval-augmented reasoning reshapes collective reliability in text-based radiology question answering.Patterns (New York, N.Y.) · 2026Article
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- Validating LLM judges for automated oversight of patient communication.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Retrieval-Augmented Generation in Radiology: A Scoping Review of Architectures, Imaging Applications, and Directions for Equitable Deployment.Journal of imaging informatics in medicine · 2026Review
- SteuerLLM: local specialized large language model for German tax law analysis.Scientific reports · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional Radiology.Cardiovascular and interventional radiology · 2026Article
- Zero-shot performance of selected large language and multimodal models on the 2023 Brazilian Portuguese medical residency exam.Scientific reports · 2026Article
- Large language model-based evaluation of the impact of gender in medical research.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
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Authors and funding
12 authors.
Funding
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
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.