Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

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Abstract: Background 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. Objectives 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. Method 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. Conclusion 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.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine ; day:21 ; month:01 ; year:2022
Methods of information in medicine ; (21.01.2022)

Contributor
Torkzadehmahani, Reihaneh
Nasirigerdeh, Reza
Blumenthal, David B.
Kacprowski, Tim
List, Markus
Matschinske, Julian
Spaeth, Julian
Wenke, Nina Kerstin
Baumbach, Jan

DOI
10.1055/s-0041-1740630
URN
urn:nbn:de:101:1-2022031010221175825092
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:29 AM CEST

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Associated

  • Torkzadehmahani, Reihaneh
  • Nasirigerdeh, Reza
  • Blumenthal, David B.
  • Kacprowski, Tim
  • List, Markus
  • Matschinske, Julian
  • Spaeth, Julian
  • Wenke, Nina Kerstin
  • Baumbach, Jan

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