向公平意识和保护隐私的医疗保健增强的协作学习
Feilong Zhang1, Deming Zhai1, Guo Bai2
1Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Nature communications
|March 24, 2025
概括
由于计算资源的不平等,医疗保健中的联邦学习 (FL) 面临公平性问题. 本研究引入了一个资源适应性框架,以确保机构间公平的AI模型参与和绩效.
科学领域:
- 医疗保健中的人工智能
- 机器学习伦理学 机器学习伦理学
- 医疗信息学 医疗信息学
背景情况:
- 人工智能在医疗保健中的整合引发了隐私和公平性的担忧.
- 联合学习 (FL) 允许在没有直接访问患者记录的情况下对分布式数据进行协作模式培训,从而增强隐私.
- 医疗机构在计算资源方面的差异在FL参与和模型性能方面造成了不公平.
研究的目的:
- 解决资源有限和资源丰富的机构在联合学习中的绩效差距.
- 为医疗保健中公平的协作学习提出一个新的资源适应性框架.
- 确保公平参与,并为所有机构提高人工智能驱动的医疗保健成果.
主要方法:
- 合作学习的资源适应性框架的开发.
- 框架的动态调整,以适应医疗机构的不同计算能力.
- 实施战略,确保公平参与,减轻绩效差异.
主要成果:
- 拟议的框架提高了模型的准确性,并保护了患者的隐私.
- 它确保公平获得可靠和高效的AI驱动的医疗保健解决方案.
- 它可以动态调整不同的计算能力,促进公平参与.
结论:
- 资源适应性框架有效地解决了医疗保健联合学习中的公平性和绩效差距.
- 它促进公平获得先进的人工智能医疗解决方案,减少差距.
- 这种方法支持可靠和高效的AI部署在不同的医疗保健环境.
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