Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.

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.