发现大型语言模型如何归因心血管风险的偏见和变化
Justine Tin Nok Chan1, Ray Kin Kwek2
1School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
Frontiers in digital health
|December 25, 2025
概括
大型语言模型 (LLM) 在心血管风险评估中显示偏差,将更高的风险归因于男性和黑人患者. 他们的决定因伴随疾病而异,强调需要仔细评估以防止健康不平等.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持系统 临床决策支持系统
- 健康 公平 研究 健康 公平 研究
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到医疗应用中.
- 然而,它们在归因心血管风险方面的表现,特别是关于人口和临床因素的表现,尚不清楚.
- 这种差距需要对LLM在这个关键领域的决策过程进行检查.
研究的目的:
- 调查一个特定的LLM (ChatGPT 4.0 mini) 如何在各种人口和临床领域分配相对心血管风险.
- 评估LLM的一致性和风险归因中的潜在偏见.
- 探索特定临床因素 (如BMI,糖尿病,抑郁症,吸烟,高脂血症) 对LLM风险评估的影响.
主要方法:
- 设计了一套结构化的提示,涵盖六个领域:一般心血管风险,BMI,糖尿病,抑郁症,吸烟和高脂血症.
- 提示以三份的形式提交给ChatGPT 4.0 mini.
- 中立提示评估了基线风险归因,而比较提示评估了特定领域被纳入时风险分配的变化.
主要成果:
- 在中立提示中,LLM通常将更高的心血管风险归因于男性而不是女性,以及与白人患者相比,与黑人患者相比,与白人患者相比.
- 在比较分析中,基于性别的风险归因在六个领域中的两个领域发生了变化 (例如,抑郁症的风险是平等的;吸烟男性的风险更高).
- 基于种族的风险归因保持一致,LLM始终将黑人患者确定为高风险患者. 在重复运行中观察到高一致性 (ICC=0.949).
结论:
- 该LLM在不同领域的心血管风险归因中展示了偏见和变化.
- 虽然基于性别的风险看法可能受到伴随性疾病的影响,但基于种族的看法显著稳定.
- 这些发现强调了在临床环境中严格评估LLM的必要性,以减轻延续现有的健康不平等的风险.
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