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

This report reviews methods for preparing imaging data for artificial intelligence applications, focusing on the need for robust privacy protection through de-identification, federated learning, and synthetic data generation, while highlighting the potential risks associated with these approaches.