缓解高收入和中低收入国家之间的机器学习偏差,以提高模型公平性和通用性
Jenny Yang1, Lei Clifton2, Nguyen Thanh Dung3
1Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, England. jenny.yang@eng.ox.ac.uk.
Scientific reports
|June 10, 2024
概括
医疗保健中的协作人工智能 (AI) 可以显示高收入和低至中等收入国家之间的绩效差异. 算法偏差缓解改善了AI COVID-19查在各种医院设置中的公平性.
科学领域:
- 医疗人工智能 医疗人工智能
- 全球卫生平等全球卫生平等
- 医疗信息学 医疗信息学
背景情况:
- 高收入国家 (HIC) 和中低收入国家 (LMIC) 之间的协作人工智能 (AI) 倡议正在增长.
- 低,中等和低收入国家经常面临资源限制,这使得合作对于分享专业知识和知识至关重要.
研究的目的:
- 研究医疗保健环境中协作人工智能模型的公平性和公平性.
- 展示数据不平衡如何导致HIC和LMIC医院之间的AI绩效结果分歧.
- 评估算法偏差缓解在提高AI公平性方面的有效性.
主要方法:
- 使用了一个现实世界的COVID-19查案例研究.
- 他们使用了来自四家英国医院 (HIC) 和一家越南医院 (LMIC) 的数据集.
- 实施了算法级偏差缓解技术,并与基准进行了比较.
主要成果:
- 协作人工智能方法在HIC和LMIC设置中表现出不同的性能结果,特别是在数据不平衡的情况下.
- 实施算法偏差缓解显著提高了HIC和LMIC网站之间的结果公平性.
- 保持了高诊断灵敏度,同时提高了公平性.
结论:
- 缓解算法偏差对于确保国际协作医疗保健项目中公平的AI性能至关重要.
- 公平干预可以弥合各种医疗保健系统的AI诊断工具的绩效差距.
- 这项研究强调了解决全球卫生应用中人工智能的偏见的重要性.
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