超越空细胞:脱离背景的医疗保健数据的威胁
Aya El Mir1, Eric Bezerra de Sousa2, Ignacio Mesina-Estarrón3
1Department of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
PLOS digital health
|January 13, 2026
概括
医疗保健数据的不准确性源于复杂的社会经济问题,而不仅仅是数据输入错误. 解决这些问题需要了解数据上下文,以防止人工智能扩大健康不平等.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能伦理学 人工智能伦理学
- 卫生社会学 卫生社会学
背景情况:
- 医疗保健数据的不准确性通常被视为可以通过统计方法解决的技术问题.
- 这些不准确性深深植根于社会经济和地缘政治因素,而不是仅仅是数据输入错误.
- 传统的统计方法无法捕捉影响数据收集的背景.
研究的目的:
- 检查如何脱离上下文的医疗保健数据和人工智能放大了健康不平等.
- 为医疗保健中更道德和更具代表性的AI提出上下文意识的策略.
- 将重点从统计方便转移到社会基础的数据处理.
主要方法:
- 对医疗保健中的传统数据建模方法进行批判性检查.
- 分析人工智能系统如何在脱离上下文的数据上进行训练,从而加强健康差异.
- 关于包含元数据和健康的社会决定因素的上下文意识方法的文献综述.
主要成果:
- 传统的统计方法不足以解决医疗数据收集中的上下文因素.
- 在脱离上下文的数据上训练的AI系统在规模上加剧了现有的健康不平等.
- 情境感知方法为更公平的AI系统提供了潜力.
结论:
- 仅仅依靠统计方法来确定数据的不准确性,就忽略了关键的社会经济和地缘政治影响.
- 了解和整合数据上下文对于减轻人工智能扩大健康差异至关重要.
- 跨学科,以社会为基础的策略对于医疗保健中的道德AI发展至关重要.
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