ArticleRadiology. Artificial intelligence2025
RadioRAG: Online Retrieval-Augmented Generation for Radiology Question Answering.
Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation.JMIR research protocols · 2026Article
- Resolution-dependent self-supervised transfer in chest radiograph classification.Communications medicine · 2026Article
- 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
- Zero-shot performance of selected large language and multimodal models on the 2023 Brazilian Portuguese medical residency exam.Scientific reports · 2026Article
- How to bring generative AI to oncology practice.ESMO real world data and digital oncology · 2026Review
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
- Article
- Multi-step retrieval and reasoning improves radiology question answering with large language models.NPJ digital medicine · 2025Article
- Two-stage prompting framework with predefined verification steps for evaluating diagnostic reasoning tasks on two datasets.NPJ digital medicine · 2025Article
- Large language model chatbots as sources of pediatric anesthesia health advice: An evaluation of reliability and readability.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Purpose To evaluate diagnostic accuracy of various large language models (LLMs) when answering radiology-specific questions with and without access to additional online, up-to-date information via retrieval-augmented generation (RAG). Materials and Methods The authors developed radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. RAG incorporates information retrieval from external sources to supplement the initial prompt, grounding the model's response in relevant information. Using 80 questions from the RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions with reference standard answers, LLMs (GPT-3.5-turbo [OpenAI], GPT-4, Mistral 7B, Mixtral 8×7B [Mistral], and Llama3-8B and -70B [Meta]) were prompted with and without RadioRAG in a zero-shot inference scenario (temperature ≤ 0.1, top-p = 1). RadioRAG retrieved context-specific information from
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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.