患者沟通风格对代理人工智能产生的临床建议在电子医学中的影响
medRxiv : the preprint server for health sciences
|January 23, 2026
概括
患者的语气显著影响了电子医学中的AI决策,改变了临床分拣和行政任务. 这凸显了人工智能 (AI) 医疗保健应用中的潜在偏见.
科学领域:
- 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
- 自然语言处理自然语言处理.
背景情况:
- 电子医药的采用正在增加,促使卫生系统评估大型语言模型 (LLM) 以执行诸如消息分类等任务.
- 患者沟通语调对人工智能驱动的临床和行政决策的影响在很大程度上仍未得到研究.
研究的目的:
- 调查患者的沟通语调是否单独影响人工智能产生的临床和行政决策.
- 量化不同沟通风格对人工智能输出对模拟患者信息的影响.
主要方法:
- 创建了1000个临床医生验证的小贴士 (500个临床,500个病假) 并以八种沟通风格呈现.
- 五个代理的LLM生成了用于分拣紧急情况和病假决定的结构化输出.
- 统计分析 (基平方,t测试与FDR校正) 评估了与中性音调的差异;40个真实患者电子邮件作为外部验证.
主要成果:
- 患者的语气明显改变了12万次运行的AI输出.
- 紧急,威胁或要求的语气增加了紧急护理建议 (14%至37-63%).
- 情绪化的语调增加了同情 (62%至70-86%),而威胁的语调减少了病假的批准 (58%至50%).
结论:
- 代理的LLM将患者的语气作为临床输入,影响分拣,处方和病假决定.
- 基调敏感的人工智能决策可能引入隐藏的偏见,影响资源分配,并促进电子医疗的滥用.
- 现实世界的验证证实了这些定向效应,强调了在AI医疗保健工具中减轻偏差的必要性.
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