公共卫生中的联合学习:对分散,公平和安全的疾病预防方法的系统审查
Sayed Tariq Shah1, Zulfiqar Ali2, Muhammad Waqar3
1Department of Computer Science, Abasyn University, Islamabad 44000, Pakistan.
Healthcare (Basel, Switzerland)
|November 13, 2025
概括
联合学习 (FL) 为公共卫生提供保护隐私的分析,使得在没有数据共享的情况下能够预防疾病. 主要挑战包括数据异质性和互操作性,需要进一步开发以实现公平部署.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 公共卫生中的集中人工智能面临着数据共享和治理障碍.
- 联合学习 (FL) 允许在不集中敏感数据的情况下进行模型培训.
研究的目的:
- 审查FL在人口和公共卫生疾病预防方面的应用.
- 将FL在这个领域的好处,挑战和政策影响映射出来.
主要方法:
- 系统的文献搜索 (PubMed,Scopus等等) 从2020年1月至2025年6月.
- 在查和数据提取方面遵循PRISMA 2020指南.
- 使用适应的MMAT和AI报告框架进行质量评估.
主要成果:
- 19项研究使用横向FL,主要用于传染病 (例如COVID-19,结核病).
- 益处包括增强的隐私,数据通用性,实时情报和风险分层.
- 发现的障碍:非IID数据,互操作性问题,资源限制以及公平性/透明性问题.
结论:
- FL显示了对安全,公平和可扩展的公共卫生分析的承诺,尊重数据主权.
- 优先事项:解决数据异质性问题,建立互操作标准,确保公平性,并提供监管指导.
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