  Artificial intelligence (AI) models are increasingly used in the medical
domain. However, as medical data is highly sensitive, special precautions to
ensure its protection are required. The gold standard for privacy preservation
is the introduction of differential privacy (DP) to model training. Prior work
indicates that DP has negative implications on model accuracy and fairness,
which are unacceptable in medicine and represent a main barrier to the
widespread use of privacy-preserving techniques. In this work, we evaluated the
effect of privacy-preserving training of AI models regarding accuracy and
fairness compared to non-private training. For this, we used two datasets: (1)
A large dataset (N=193,311) of high quality clinical chest radiographs, and (2)
a dataset (N=1,625) of 3D abdominal computed tomography (CT) images, with the
task of classifying the presence of pancreatic ductal adenocarcinoma (PDAC).
Both were retrospectively collected and manually labeled by experienced
radiologists. We then compared non-private deep convolutional neural networks
(CNNs) and privacy-preserving (DP) models with respect to privacy-utility
trade-offs measured as area under the receiver-operator-characteristic curve
(AUROC), and privacy-fairness trade-offs, measured as Pearson's r or
Statistical Parity Difference. We found that, while the privacy-preserving
trainings yielded lower accuracy, they did largely not amplify discrimination
against age, sex or co-morbidity. Our study shows that -- under the challenging
realistic circumstances of a real-life clinical dataset -- the
privacy-preserving training of diagnostic deep learning models is possible with
excellent diagnostic accuracy and fairness.
