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艾米丽与拉基沙:量化人工智能产生的卵巢癌护理计划中的社会经济和结构差异
Muhammed İnan1, Serra Akar İnan2, Cenk Aypak1
1University of Health Sciences, Ankara Etlik City Hospital, Department of Family Medicine. Ankara, Türkiye.
Gynecologic oncology
|February 25, 2026
概括
大型语言模型 (LLM) 显示癌症护理转诊的偏见,不成比例地影响黑人农村患者. 人工智能必须与结构性需求保持一致,以防止健康不平等.
科学领域:
- 人工智能在医学中的应用
- 健康 公平 研究 健康 公平 研究
- 瘤学的决策支持
背景情况:
- 大型语言模型 (LLM) 越来越多地用于瘤学.
- 在LLMs中隐含的偏见可能会加剧健康差异.
- 这项研究调查了高级卵巢癌LLM生成的转诊计划中的结构偏差.
研究的目的:
- 量化LLM生成的转介计划中的结构偏差.
- 检查人口学代理人的影响 (种族,地理) 对LLM输出.
- 通过自动化结构能力评分 (ASCS) 管道评估LLM绩效.
主要方法:
- 对320个LLM生成的转介计划进行横截面的随机算法审计.
- 使用了2x2的因数图形设计,使用了种族 (艾米丽对拉基沙) 和地理 (波士顿对印第安诺拉) 的代理.
- 评估了23个指标的反应,包括临床一致性,优先级和语言音调.
主要成果:
- 在LLM中表现出"医疗结构分离",保持临床准确性,但表现出结构偏差.
- 算法对黑人/农村现象型严重无能.
- 模型在"Lakisha"和"Emily"之间发出意外成本警告的可能性是16.8倍,表明由种族驱动的"财务固定".
结论:
- 目前的LLM在临床上是准确的,但在结构上是有偏见的,有风险"算法红线".
- 为了安全部署,人工智能必须超越生物医学忠实,转向"结构调整".
- 人工智能应该作为一种认知假肢来缓解,而不是根深蒂固,医疗保健中的系统差异.
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