在基于人工智能的应用中,健康数据集标准的价值
Anmol Arora1, Joseph E Alderman2,3,4, Joanne Palmer3,4
1School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Nature medicine
|October 26, 2023
概括
人工智能医疗设备中的算法偏差可能会加剧健康不平等. 本研究探讨了数据多样性标准和专家观点,以制定医疗保健中公平的人工智能指南.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 健康 公平 研究 健康 公平 研究
背景情况:
- 人工智能医疗设备 (AIMD) 在医疗保健中越来越多地用于诊断和资源配置.
- 在AIMDs中的算法偏差带来了延续和加剧现有的健康不平等的重大风险.
- 数据集策划,研究参与和访问方面的系统性问题有助于这种偏见.
研究的目的:
- 探索现有的标准,框架和最佳实践,以确保健康数据集中的数据多样性.
- 收集和分析利益相关者关于偏见,健康公平和AIMD的最佳实践的观点.
- 为制定基于共识的指导方针,为医疗保健中的公平人工智能提供信息.
主要方法:
- 对医疗保健数据集的当前标准,框架和最佳实践进行系统审查.
- 对利益相关者对偏见,健康公平和AIMDs的看法进行了调查和专题分析.
- 探索有关数据多样性的现有文献和专家意见.
主要成果:
- 文献广泛描述了医疗保健AI中数据集多样性的需要.
- 专家们普遍支持为数据多样性制定全面指南.
- 专家们对这些指导方针的实际实施有不同的意见.
结论:
- 解决数据多样性对于减轻AIMD中的算法偏差至关重要.
- 制定强有力的指导方针需要进一步就实际实施战略达成共识.
- 这项研究为STANDING Together倡议的透明数据多样性标准做出了贡献.
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