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<p>Effectively managing evidence-based information is increasingly challenging. This study tested large language models (LLMs), including document- and online-enabled retrieval-augmented generation (RAG) systems, using 13 recent neurology guidelines across 130 questions. Results showed substantial variability. RAG improved accuracy compared to base models but still produced potentially harmful answers. RAG-based systems performed worse on case-based than knowledge-based questions. Further refinement and improved regulation is needed for safe clinical integration of RAG-enhanced LLMs.</p>

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Evaluating base and retrieval augmented LLMs with document or online support for evidence based neurology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月4日

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  • Lars Masanneck,
  • Sven G. Meuth,
  • Marc Pawlitzki

摘要

Effectively managing evidence-based information is increasingly challenging. This study tested large language models (LLMs), including document- and online-enabled retrieval-augmented generation (RAG) systems, using 13 recent neurology guidelines across 130 questions. Results showed substantial variability. RAG improved accuracy compared to base models but still produced potentially harmful answers. RAG-based systems performed worse on case-based than knowledge-based questions. Further refinement and improved regulation is needed for safe clinical integration of RAG-enhanced LLMs.