临床医学中的人类大语言模型协作:系统性审查和元分析
Guoyong Wang1, Kaijun Zhang2, Jiyue Jiang3
1Department of hemangioma and vascular malformation, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
NPJ digital medicine
|January 28, 2026
概括
人与人工智能的协作显示出改善临床任务的潜力,但目前的证据是初步和不确定的. 需要进一步的研究来确认好处,并解决文档中的高事实错误率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 提供了增强临床推理,文档和解释的潜力.
- 人与人工智能的协作 (H+AI) 是医疗保健领域的一个新兴范式.
- 系统审查方法对于评估H+AI有效性至关重要.
研究的目的:
- 系统地比较人类-人工智能 (H+AI) 协作与人类 (H) 工作流程的有效性.
- 评估H+AI对诊断准确性,管理,文档质量和时间效率的影响.
- 确定用于临床应用的H+AI未来研究领域.
主要方法:
- 按照PRISMA 2020指南进行系统的文献审查.
- 搜索了四个数据库,截至2025年6月28日,包括10项同行评审的研究.
- 分析了诊断/解释准确性,复合分数和时间效率.
主要成果:
- 在诊断/解释准确性方面,H+AI的积极但不显著的趋势 (RR 1.59).
- 综合诊断/管理得分的统计显著改善 (MD +4.88 pp),不确定性高 (PI -31.65至41.42).
- 时间效率没有显著差异 (MD +0.4分钟);文档质量有所改善,但事实错误率仍然很高 (~26-36%).
结论:
- 目前关于临床环境中H+AI的证据是初步的,高度不确定的.
- 文件中的实际错误仍然存在,破坏了潜在的质量提升.
- 建议预先注册的实用试验,重点关注安全性,错误指标和不确定性沟通.
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