在临床文档中使用大型语言模型:范围审查
Brigitte Fong Yeong Woo1, Kenrick Cato2, Hannah Cho3
1University of Pennsylvania School of Nursing, Philadelphia, United States of America; The Commonwealth Fund, New York, United States of America; Nursing Administration, Ng Teng Fong General Hospital, Singapore, Singapore.
大型语言模型 (LLM) 可以提高临床文档的效率和可读性. 然而,对准确性,道德和临床医生信任的谨慎关注对于其安全融入医疗保健至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 改善临床文档 改善临床文档
背景情况:
- 临床文档的复杂性导致临床医生的倦怠.
- 大型语言模型 (LLM) 为文档任务提供了潜在的解决方案.
- 有关LLM准确性,偏见,隐私和监管风险存在担忧.
研究的目的:
- 在临床文档中对LLM应用的当前文献进行映射.
- 评估LLM在这个领域的好处,局限性和伦理考虑.
主要方法:
- 根据阿克西和奥马利的框架进行范围审查.
- 搜索了五个数据库 (PubMed,Scopus,CINAHL,Cochrane,IEEE Xplore) 的英语文学研究 (2009-2025年).
- 独立选,数据提取和质量评估由多个审稿人进行;叙事综合.
主要成果:
- 分析了41项研究,主要来自美国,重点关注笔记生成和摘要.
- LLM 显示了在文档中节省高达 40% 的时间的潜力,提高了效率和可读性.
- 担忧包括事实上的不准确性,'幻觉',偏见,隐私以及临床医生对可靠性和细微差别的混合看法.
结论:
- 在提高临床文档效率,标准化和清晰度方面,LLM是有前途的.
- 安全的整合需要严格关注准确性,道德保障和临床医生的信任.
- 法律学应该增强,而不是取代临床推理,需要共同设计和人工智能素养.
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