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RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

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arxiv 2407.15621 v3 pith:UVGIEXZE submitted 2024-07-22 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords radioragllmsaccuracydataansweringradiologyacrossinformation
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating outside data sources. While previous RAG systems used pre-assembled, fixed databases with limited flexibility, we have developed Radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. We evaluate the diagnostic accuracy of various LLMs when answering radiology-specific questions with and without access to additional online information via RAG. 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, GPT-4, Mistral-7B, Mixtral-8x7B, and Llama3 [8B and 70B]) were prompted with and without RadioRAG in a zero-shot inference scenario RadioRAG retrieved context-specific information from Radiopaedia in real-time. Accuracy was investigated. Statistical analyses were performed using bootstrapping. The results were further compared with human performance. RadioRAG improved diagnostic accuracy across most LLMs, with relative accuracy increases ranging up to 54% for different LLMs. It matched or exceeded non-RAG models and the human radiologist in question answering across radiologic subspecialties, particularly in breast imaging and emergency radiology. However, the degree of improvement varied among models; GPT-3.5-turbo and Mixtral-8x7B-instruct-v0.1 saw notable gains, while Mistral-7B-instruct-v0.2 showed no improvement, highlighting variability in RadioRAG's effectiveness. LLMs benefit when provided access to domain-specific data beyond their training data. RadioRAG shows potential to improve LLM accuracy and factuality in radiology question answering by integrating real-time domain-specific data.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MedOrchestra: A Hybrid Cloud-Local LLM Approach for Clinical Data Interpretation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A cloud-local hybrid, where the cloud writes subtask prompts offline and a local model executes them on patient data, reached 70-85% staging accuracy, above local baselines and clinicians.

  2. Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases

    cs.CY 2025-06 conditional novelty 5.0 of 10

    In a physician-rated crowdsourced study, 76% of LLM responses to everyday health queries were valid, with GPT-4o highest (85%) and Llama3-8b lowest (50%); RAG did not consistently improve responses.

  3. From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine

    cs.AI 2025-02 conditional novelty 3.0 of 10

    A PRISMA-ScR scoping review of 144 studies finds the field shifting from text-only LLMs to multimodal AI in medicine, with evaluation and data diversity still the main bottlenecks.

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