Rethinking Privacy in Medical Imaging AI: From Metadata and Pixel-Level Identification Risks to Federated Learning and Synthetic Data Challenges

Abstract

Metadata, which refers to nonimage information such as patient identifiers, acquisition parameters, and institutional details, have long been the primary focus of de-identification efforts when constructing datasets for artificial intelligence applications in medical imaging. However, it is now evident that information intrinsic to the image itself, at the pixel level (eg, intensity values), can also be exploited by deep learning models, potentially revealing sensitive patient data and posing privacy risks. This report discusses both metadata and sources of identifiable information in medical imaging studies, highlighting the potential risks of overlooking their presence. Privacy-preserving approaches such as federated learning and synthetic data generation are also reviewed, with emphasis on their limitations-particularly vulnerabilities to model inversion and inference attacks-that must be considered when developing and deploying artificial intelligence in medical imaging. <b>Keywords:</b> Privacy, Metadata, Synthetic, Federated Learning, Anonymization De-identification ©RSNA, 2025.

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