Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

arXiv:2302.01622v4 Announce Type: replace-cross
Abstract: 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, …

ai models artificial artificial intelligence arxiv cs.ai cs.cr cs.cv cs.lg data differential privacy domain eess.iv fair imaging intelligence introduction large medical medical data medical imaging model training preservation privacy private protection scale sensitive special standard training