种族和民族数据的标准化和准确性:医疗AI的公平影响
Alexandra Tsalidis1, Lakshmi Bharadwaj2,3, Francis X Shen4,5,6
1Future of Life Institute, Brussels, Belgium.
PLOS digital health
|May 29, 2025
概括
电子健康记录 (EHR) 中准确的患者种族和种族 (r/e) 数据对于医疗保健中可靠的人工智能 (AI) 至关重要. 这项研究确定了数据质量问题,并提出了更好的AI模型开发的解决方案.
科学领域:
- 医疗保健人工智能的人工智能
- 医疗信息学 医疗信息学
- 数据质量数据质量
背景情况:
- 人工智能 (AI) 在医疗保健中的整合引发了对算法偏见的担忧.
- 电子健康记录 (EHR) 中患者种族和种族 (r/e) 数据的准确性和代表性往往是被忽视的质量控制问题.
研究的目的:
- 批判性地检查导致EHR中不准确和不代表性的r/e数据集的因素.
- 为改善医疗保健中的 r/e 数据质量提出可行的步骤.
主要方法:
- 分析种族/种族分类中的概念不确定性.
- 对数据收集实践和EHR标准的评估.
- 识别患者错误分类问题.
主要成果:
- 不准确的r/e数据源于分类模两可,有缺陷的收集方法,电子健康记录标准和患者错误分类.
- 提出了一项双重行动计划,以提高r/e数据质量.
结论:
- 医疗保健系统和人工智能研究人员需要最佳实践来提高r/e数据的准确性.
- 医疗人工智能开发人员必须透明地保证其r/e数据的质量.
- 为了在人工智能驱动的医疗保健中保持伦理和科学完整性,必须立即采取行动.
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