Scholarlink

<p>Generative artificial intelligence has brought disruptive innovations in health care but faces certain challenges. Retrieval-augmented generation (RAG) enables models to generate more reliable content by leveraging the retrieval of external knowledge. In this perspective, we analyze the possible contributions that RAG could bring to health care in equity, reliability, and personalization. Additionally, we discuss the current limitations and challenges of implementing RAG in medical scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Retrieval-augmented generation for generative artificial intelligence in health care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月25日

查看全文
  • Rui Yang,
  • Yilin Ning,
  • Emilia Keppo,
  • Mingxuan Liu,
  • Chuan Hong,
  • Danielle S. Bitterman,
  • Jasmine Chiat Ling Ong,
  • Daniel Shu Wei Ting,
  • Nan Liu

摘要

Generative artificial intelligence has brought disruptive innovations in health care but faces certain challenges. Retrieval-augmented generation (RAG) enables models to generate more reliable content by leveraging the retrieval of external knowledge. In this perspective, we analyze the possible contributions that RAG could bring to health care in equity, reliability, and personalization. Additionally, we discuss the current limitations and challenges of implementing RAG in medical scenarios.