ArticleRadiology. Artificial intelligence2025
Enhancing Large Language Models with Retrieval-Augmented Generation: A Radiology-Specific Approach.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Locally deployed context-aware chatbot outperforms generic large language models for guideline-concordant pediatric imaging recommendations.Pediatric radiology · 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
- Open-source offline-deployable retrieval-augmented large language model for assisting pancreatic cancer staging.Japanese journal of radiology · 2026Article
- Enhancing pancreatic cancer staging with large language models: the role of retrieval-augmented generation.Radiological physics and technology · 2026Article
- Development of retrieval-augmented generation-based large language model for drug-induced liver injury using Livertox data.Hepatology communications · 2026Article
- Impact of AI-assisted decision support on radiological diagnosis of jawbone lesions.Clinical oral investigations · 2026Article
- Low-energy small language models with retrieval-augmented generation can surpass large-model performance in rheumatology.Frontiers in medicine · 2026Article
- Keeping AI in medicine and radiology within the framework of scientific method: measuring to close the epistemic gap.Insights into imaging · 2025Article
- Context Is Everything: Understanding Variable LLM Performance for Radiology Retrieval-Augmented Generation.Radiology. Artificial intelligence · 2025Article
- Large language model chatbots as sources of pediatric anesthesia health advice: An evaluation of reliability and readability.Digital healthArticle
- Retrieval-Augmented Generation in Oncology: Promises, Pitfalls, and Early Applications.AI in precision oncologyArticle
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Retrieval-augmented generation (RAG) is a strategy to improve the performance of large language models (LLMs) by providing an LLM with an updated corpus of knowledge that can be used for answer generation in real time. RAG may improve LLM performance and clinical applicability in radiology by providing citable, up-to-date information without requiring model fine-tuning. In this retrospective study, a radiology-specific RAG system was developed using a vector database of 3689
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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.