GPT-4o-mini中的隐形偏差:检测人工智能生成的患者消息中的差异
Christopher Reagen1, Katelyn M Brown2, Thomas Hocker3
1School of Medicine, University of Missouri-Kansas City, 2411 Holmes St, Kansas City, MO, 64108, USA. cjr8zx@umsystem.edu.
Journal of medical systems
|October 7, 2025
概括
人工智能 (AI),特别是大型语言模型 (LLM),可以增强患者护理,但可能含有隐藏的偏见. 这项研究发现,患者的性别和宗教偏好显著影响了人工智能生成的沟通得分,突出了潜在的差异.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 显示出越来越多的改善患者护理的承诺.
- 然而,人工智能培训数据中的偏见可能导致医疗通信中的有害差异.
- 评估这些偏见对于在医学中安全实施人工智能至关重要.
研究的目的:
- 开发和提出一种用于评估LLMs偏见的新方法.
- 用合成患者数据评估人工智能产生的患者通信差异.
- 识别可能影响AI通信质量的特定患者特征.
主要方法:
- 利用GPT-4o-mini从合成医疗记录生成患者通信.
- 系统地创建各种合成患者数据,包括人口统计和历史信息.
- 使用GPT-4o-mini对人工智能产生的沟通进行评分,以同理心,准确性,清晰性,专业性,尊重和鼓励.
- 分析了与患者属性相关的评分差异,以检测潜在的偏见.
主要成果:
- 患者的性别和宗教偏好在统计学上显著影响了人工智能产生的通信质量得分.
- 确定了与人工智能沟通差异相关的特定患者病史组件.
- 提出的方法成功地检测出了LLM产生的患者互动中的潜在偏差.
结论:
- 该研究提出了一种新的方法,用于在LLM中检测偏差,使用合成数据和基于AI的评分.
- 研究结果表明,像GPT-4o-mini这样的LLM可能会表现出与患者人口统计和个人病史相关的偏见.
- 建议进行进一步的研究,以完善评估标准,并评估更广泛的临床应用的LLM.
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