Institute for Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany.
Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Methods Inf Med. 2022 Jun;61(S 01):e12-e27. doi: 10.1055/s-0041-1740630. Epub 2022 Jan 21.
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.
However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.
This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.
As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.
人工智能（AI）已经在许多科学领域得到了成功应用。在生物医学领域，人工智能已经显示出了巨大的潜力，例如，在解释下一代测序数据和设计临床决策支持系统方面。
然而，在敏感数据上训练 AI 模型引发了对个体参与者隐私的担忧。例如，全基因组关联研究的汇总统计数据可用于确定个体在给定数据集中的存在或缺失。这种相当大的隐私风险导致了对获取基因组和其他生物医学数据的限制，这对合作研究有害，并阻碍了科学进步。因此，人们已经做出了很大的努力来开发能够在保护个人隐私的同时从敏感数据中学习的 AI 方法。
本文提供了一个关于生物医学中隐私保护 AI 技术的最新进展的结构化概述。它将最重要的最先进方法置于统一的分类法中，并讨论了它们的优缺点和开放性问题。
作为最有前途的方向，我们建议将联邦机器学习作为一种更具可扩展性的方法与其他附加隐私保护技术结合使用。这将允许以分布式方式合并优势，为生物医学应用提供隐私保证。然而，还需要更多的研究，因为混合方法会带来新的挑战，例如额外的网络或计算开销。
