在医学大语言模型中评估和解决人口差异:系统性审查
Mahmud Omar1, Vera Sorin2, Reem Agbareia3
1The Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA. Mahmudomar70@gmail.com.
International journal for equity in health
|February 26, 2025
概括
人口偏见,特别是关于性别和种族的偏见,在医疗保健中使用的大型语言模型中很普遍. 对这些人工智能偏见的有效缓解策略仍在开发中,需要进一步研究公平的人工智能.
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
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 在医疗保健应用中表现有前途.
- 人们担心它们有可能加剧现有的健康差距.
- 这项研究系统地审查了LLMs内人口偏见的研究.
研究的目的:
- 确定在LLMs中普遍存在的人口偏见的类型.
- 评估目前测量这些偏差的方法.
- 评估拟议减缓策略的有效性.
主要方法:
- 2018年1月 - 2024年7月发表的研究的系统文献综述.
- 搜索了五个主要的数据库进行同行评审的研究.
- 包括评估与性别,种族,种族和年龄有关的偏见的研究.
- 使用乔安娜·布里格斯研究所的批判性评估工具评估研究质量.
主要成果:
- 包括24项研究;22项 (91.7%) 报告的人口偏见.
- 性别偏见是最常见的 (93.7%的相关研究),其次是种族/民族偏见 (90.9%).
- 快速工程是一种常见的缓解策略,成功率可变;出版偏差可能会影响研究结果.
结论:
- 在医学领域的LLM中,人口偏见很普遍.
- 偏差检测方法正在进步,但有效的缓解仍然是一个挑战.
- 解决LLM偏见对于医疗保健中公平的AI至关重要;未来的研究需要更广泛的范围.
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