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<p>Retrieval-augmented generation (RAG) aims to curb large language models (LLMs) hallucinations, yet its conversational reliability is uncertain. We tested a clinical RAG by executing the same query 100 times at varying dialogue lengths. The hallucination rate surged from 5%(no history) to 40% with just 10 prior exchanges, revealing a critical failure mode. Rigorous conversational testing is essential for patient safety before clinical deployment of RAG systems.</p>

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RAG in clinical practice: a cautionary tale of AI ‘Truthfulness’

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • HyoJe Jung,
  • Kyusok Cho,
  • Tae Joon Jun,
  • Young-Hak Kim

摘要

Retrieval-augmented generation (RAG) aims to curb large language models (LLMs) hallucinations, yet its conversational reliability is uncertain. We tested a clinical RAG by executing the same query 100 times at varying dialogue lengths. The hallucination rate surged from 5%(no history) to 40% with just 10 prior exchanges, revealing a critical failure mode. Rigorous conversational testing is essential for patient safety before clinical deployment of RAG systems.