Summary of Radiorag: Factual Large Language Models For Enhanced Diagnostics in Radiology Using Online Retrieval Augmented Generation, by Soroosh Tayebi Arasteh et al.
RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generation
by Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem, Robert Siepmann, Lisa Adams, Dyke Ferber, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets.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. Our Radiology RAG (RadioRAG) framework retrieves data from authoritative radiologic online sources in real-time, enhancing diagnostic accuracy and factuality in radiological question answering. We evaluated various LLMs’ performance when answering radiology-specific questions with and without access to additional online information via RadioRAG. Using 80 questions from the RSNA Case Collection and 24 expert-curated questions, we found that RadioRAG improved diagnostic accuracy across most LLMs, with relative accuracy increases ranging up to 54% for different models. Our results demonstrate the effectiveness of RAG in radiology, particularly in breast imaging and emergency radiology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make large language models more accurate by giving them access to real-time information from trusted sources. Right now, these models can be wrong or outdated because they only have the information they were trained on. Our new system, called RadioRAG, helps fix this problem by letting models look up answers online in real-time. We tested RadioRAG with different language models and found that it helped them get the right answer more often. This is especially important for radiology, where getting the diagnosis right can be life or death. |
Keywords
* Artificial intelligence * Question answering * Rag * Retrieval augmented generation
