Health AI Central
Peer-Reviewed Publication
Nature2026June 24, 2026Journal Article

Disparate privacy risks from medical AI.

Moritz A Knolle1, Martin J Menten2,3,4, Friederike Jungmann2,5, Felix Meissen2, Ben Glocker4, Daniel Rueckert2,3,4, Georgios Kaissis6
1Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany. moritz.knolle@tum.de.
2Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.
3Munich Center for Machine Learning, Munich, Germany.
4Department of Computing, Imperial College London, London, UK.
5Institute for Diagnostic and Interventional Radiology, TUM University Hospital, Technical University of Munich, Munich, Germany.
6Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
PubMed DOI

Abstract

Medical artificial intelligence (AI) models hold the promise to improve global access to high-quality diagnostics1. However, the training data underlying these models often contain sensitive patient information that may be exposed through privacy attacks2-7. Previous research has primarily quantified the success of these attacks in aggregate, across all records in a dataset. Thus, the privacy risk…

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