缩小人工智能效益差距:为人口健康公平设计系统
Jessica Morley1, Luciano Floridi2
1Digital Ethics Center, Yale University, 85 Trumbull Street, CT, 06510, United States.
Public health
|February 20, 2026
概括
医疗保健中的人工智能 (AI) 显示,由于基础不足和个人层面的关注,人口健康的改善有限. 将人工智能重新调整为人口健康目标,并与各种利益相关者共同创建解决方案,对于实现其潜力至关重要.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 承诺提高医疗保健的有效性,效率和公平性,但广泛的好处和人口层面的结果改善并未始终被观察到.
- 尽管实施成功,但当前在医疗保健中的人工智能应用往往面临着扩展和展示人口健康实质性收益的挑战.
研究的目的:
- 确定医疗保健AI实施中的"福利差距"的根本原因.
- 提出一个新的AI发展和部署在医疗保健的框架,以实现人口健康目标.
主要方法:
- 分析当前人工智能医疗应用中的挑战和局限性,从2024年"人工智能时代全球健康"研讨会中得出结论.
- 对人工智能的功能和医疗保健的基础设计进行根本性的转变的论证.
主要成果:
- 医疗人工智能的"福利差距"归因于两个核心问题:基础基础设施不足和对优化个人健康而不是人口健康的不适当关注.
- 现有的政策干预措施不足以弥补这一差距;需要进行系统性重新设计.
结论:
- 人工智能必须重新用于人口层面的健康功能,以强大,系统设计的基础为基础.
- 受影响社区,医疗保健专业人员和AI开发人员共同创建AI的功能和基础,对于实现AI的人口健康潜力和防止"医疗保健AI冬季"至关重要.
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