ArticleNPJ digital medicine2025
Retrieval-augmented generation elevates local LLM quality in radiology contrast media consultation.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- 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
- The Emerging Role of Generalizable Vision-Language Models in Ovarian Cancer Diagnosis.Annals of surgical oncology · 2026Article
- An automatic consult reply system for therapeutic plasma exchange using retrieval-augmented generation.Vox sanguinis · 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
- Performance and safety of a fine-tuned small language model for pediatric emergency triage: A benchmark study.PloS one · 2026Article
- Zero-Shot PI-RADS Version 2.1 Scoring with ChatGPT-4 Turbo and Llama 3: Diagnostic Performance and Agreement with Abdominal Radiologists.Radiology. Imaging cancer · 2026Article
- LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Two-stage prompting framework with predefined verification steps for evaluating diagnostic reasoning tasks on two datasets.NPJ digital medicine · 2025Article
- Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
13 authors.
Funding
Abstract
Large language models (LLMs) demonstrate significant potential in healthcare applications, but clinical deployment is limited by privacy concerns and insufficient medical domain training. This study investigated whether retrieval-augmented generation (RAG) can improve locally deployable LLM for radiology contrast media consultation. In 100 synthetic iodinated contrast media consultations we compared Llama 3.2-11B (baseline and RAG) with three cloud-based models-GPT-4o mini, Gemini 2.0 Flash and Claude 3.5 Haiku. A blinded radiologist ranked the five replies per case, and three LLM-based judges scored accuracy, safety, structure, tone, applicability and latency. Under controlled conditions, RAG eliminated hallucinations (0% vs 8%; χ²₍Yates₎ = 6.38, p = 0.012) and improved mean rank by 1.3 (Z = -4.82, p < 0.001), though performance gaps with cloud models persist. The RAG-enhanced model remained faster (2.6 s vs 4.9-7.3 s) while the LLM-based judges preferred it over GPT-4o mini, though the radiologist ranked GPT-4o mini higher. RAG thus provides meaningful improvements for local clinical LLMs while maintaining the privacy benefits of on-premise deployment.
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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 Socratic graph.