RadioRAG: Online Retrieval-Augmented Generation for Radiology Question Answering
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View abstract on PubMed
Summary
This summary is machine-generated.Retrieval-augmented generation (RAG) enhanced large language model (LLM) accuracy in radiology question answering, outperforming human experts for some models. RAG also reduced LLM hallucinations but increased response time.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Radiology
- Natural Language Processing
Background
- Large Language Models (LLMs) show promise for medical question answering.
- LLMs can generate inaccurate information (hallucinations) without access to real-time data.
- Retrieval-augmented generation (RAG) integrates external knowledge to improve LLM factuality.
Purpose Of The Study
- To assess the diagnostic accuracy of LLMs in radiology question answering.
- To evaluate the impact of Retrieval-Augmented Generation (RAG) on LLM performance.
- To compare LLM performance with and without RAG against human expert accuracy.
Main Methods
- Developed RadioRAG, a framework for real-time data retrieval from radiologic sources.
- Evaluated multiple LLMs (GPT-3.5-turbo, GPT-4, Mistral, Llama3) with and without RadioRAG.
- Used 80 RSNA Case Collection questions and 24 expert-curated questions for assessment.
Main Results
- RadioRAG improved accuracy for GPT-3.5-turbo (74% vs 66%) and Mixtral 8x7B (76% vs 65%) on the RSNA-RadioQA dataset.
- LLM accuracy with RAG surpassed human expert performance (63%) for some models.
- RadioRAG reduced hallucinations across all tested LLMs (6%-25% rate) but increased response time fourfold.
Conclusions
- RadioRAG demonstrates potential to enhance LLM accuracy and factuality in radiology.
- Integrating real-time, domain-specific data via RAG is crucial for reliable AI in medical QA.
- Further research can optimize RAG for faster, more accurate AI-assisted radiology.
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