检测大语言模型产生的护理服务的内容和质量的社会人口学偏差:跨部分模拟研究
Nan Bai1, Yijing Yu1, Chunyan Luo1
1Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, No. 115, Donghu Road, Wuchang District, Wuhan, Hubei, China, +86 15902731922.
Journal of medical Internet research
|December 8, 2025
概括
大型语言模型 (LLM) 在护理护理计划中显示了社会人口统计偏差,影响了公平. 这项研究提供了在LLM产生的计划中存在偏见的经验证据,突出了健康差异的风险.
科学领域:
- 医疗保健中的人工智能
- 护理信息学 护理信息学
- 健康 公平 研究 健康 公平 研究
背景情况:
- 人们对大语言模型 (LLM) 产生的护理护理计划中的社会人口统计学偏见存在担忧.
- 缺乏经验证据评估LLM护理建议中的偏见.
研究的目的:
- 调查LLM产生的护理护理计划中的社会人口统计偏见.
- 评估对公平护理的影响.
主要方法:
- 使用GPT-4生成9600个护理护理计划的混合方法模拟研究.
- 对500个计划的计划内容的定量分析和临床质量的专家评估.
- 根据性别,年龄,收入,教育和居住地的不同患者资料.
主要成果:
- 在专题内容和临床质量方面,LLM护理计划显示出系统的社会人口统计差异.
- 低收入和教育程度与不太有利的计划特征有关.
- 较老的患者档案获得了更多的疼痛管理,但较少的护士培训建议.
- 专家审查表明总体质量很高,但根据城市居住区的完整性,适用性和安全性存在差异.
结论:
- 在护理计划中,LLM重现了社会人口统计学偏见,有可能加强健康不平等.
- 经验证据表明,在LLM产生的护理护理计划中存在微妙的偏见.
- 强调需要人类监督,以确保人工智能促进护理中的公平.
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