"Doing no harm" in the digital age: navigating tradeoffs and operational considerations for privacy-preserving deep learning in medicine.

Abstract

As artificial intelligence (AI) is increasingly deployed in clinical care, protecting patient privacy has become a central challenge. Mohammadi et al. examine the emerging use of privacy-enhancing technologies in medical deep learning, and look at how formal privacy guarantees interact with model performance and fairness. Their findings reveal variability in performance-privacy tradeoffs across data types and tasks, and highlight the risk that privacy constraints may widen existing disparities if not carefully evaluated. Beyond performance, privacy and equity should also be considered as equal pillars of trustworthy medical AI.