  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.
