在医学报告生成中揭示和量化大型语言模型的种族偏见
Yifan Yang1,2, Xiaoyu Liu2, Qiao Jin1
1National Institutes of Health (NIH), National Library of Medicine (NLM), National Center for Biotechnology Information (NCBI), Bethesda, MD 20894, USA.
ArXiv
|February 27, 2024
概括
在医疗保健中使用的大型语言模型 (LLM) 可能会延续偏见,预测白人患者的成本更高,并显示偏差的生存率. 解决这些人工智能偏见对于公平的患者护理至关重要.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 医疗保健的不平等 医疗保健的不平等
背景情况:
- 像GPT-3.5-turbo和GPT-4这样的大型语言模型 (LLM) 在医疗保健中具有潜力,但可能包含训练数据偏差.
- 关于这些偏差在医疗应用中的程度和影响的先前研究是有限的.
研究的目的:
- 在医疗环境中调查和分析大型语言模型中存在的偏见.
- 确定遗传偏差对医疗保健应用程序的程度和影响.
- 确定模型偏差表现的特定领域.
主要方法:
- 使用了定性和定量分析.
- 对患者背景生成中偏差的模型输出的评估.
- 评估疾病与种族的关联以及治疗建议的差异.
主要成果:
- 劳工法则表明偏见,预测白人人口的医疗保健成本更高,住院时间更长.
- 模型在关键医疗场景中表现出过于乐观的生存率.
- 在患者背景生成,疾病与种族相关性以及治疗差异方面观察到偏见.
结论:
- 这些发现突显了当前LLM中的重大偏见,影响了医疗应用中的公平性.
- 缓解这些人工智能偏见对于确保公平准确的医疗保健结果至关重要.
- 进一步的研究对于开发和实施医学AI中偏见检测和缓解策略至关重要.
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