  The utilisation of artificial intelligence in medicine and healthcare has led
to successful clinical applications in several domains. The conflict between
data usage and privacy protection requirements in such systems must be resolved
for optimal results as well as ethical and legal compliance. This calls for
innovative solutions such as privacy-preserving machine learning (PPML). We
present PriMIA (Privacy-preserving Medical Image Analysis), a software
framework designed for PPML in medical imaging. In a real-life case study we
demonstrate significantly better classification performance of a securely
aggregated federated learning model compared to human experts on unseen
datasets. Furthermore, we show an inference-as-a-service scenario for
end-to-end encrypted diagnosis, where neither the data nor the model are
revealed. Lastly, we empirically evaluate the framework's security against a
gradient-based model inversion attack and demonstrate that no usable
information can be recovered from the model.
