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Updated: Feb 12, 2026

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通过双重视角框架评估大型语言模型生成的临床摘要:回顾性观察性研究
Brian Han1, Traci Barnes1, Charitha D Reddy1
1Division of Cardiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Palo Alto, CA, United States.
JMIR AI
|February 10, 2026
概括
大型语言模型 (LLM) 总结了患者和家人的医疗笔记. 父母和医生的双重观点揭示了帮助性和可读性方面的差异,强调需要均衡的评估.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者沟通 患者沟通
背景情况:
- 大型语言模型 (LLM) 是患者和家属理解复杂医疗信息的新兴工具.
- 目前对医疗保健中的LLM的评估主要集中在临床医生评估的准确性上,可能会忽视患者的观点.
- 解释儿科心脏重症监护室 (CICU) 文档由于其复杂性而带来了独特的挑战.
研究的目的:
- 为了评估GPT-4o mini在总结儿科重症监护室笔记中的表现.
- 为了比较父母和医生对LLM生成的摘要的评估,关于可读性,临床忠实性和有用性.
- 为了确定患者和临床医生的观点之间的差异,关于LLM摘要的实用性.
主要方法:
- 使用GPT-4o迷你大语言模型总结了50个儿科重症监护室笔记.
- 医生和家长独立审查生成的摘要.
- 参与者根据可读性,临床准确性和整体帮助性对摘要进行了评分.
主要成果:
- 在家长和医生评分之间观察到显著差异,特别是关于摘要的有用性.
- 临床医生为LLM产生的内容的临床准确性提供了关键的见解.
- 父母对摘要的可读性和可理解性提供了有价值的反.
结论:
- 评估医疗保健中的LLM需要一个双重视角的框架,包括临床医生和患者的评估.
- 在临床准确性与患者理解之间取得平衡,对于在面向患者的医疗文档中有效实施LLM至关重要.
- 未来的研究应该专注于开发和验证这些综合性评估方法.
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