Principles of Deidentification
Deidentification removes or obfuscates patient identifiers from imaging data to enable research education and AI development while protecting privacy and methods include removal of direct identifiers metadata scrubbing and pixel level anonymization when needed and teams balance data utility with privacy risk
Techniques and Validation
Techniques range from metadata stripping to advanced pixel anonymization and face removal for three dimensional reconstructions and validation includes verifying that no reidentifiable information remains and that clinical utility is preserved and documentation of methods and of residual risk supports governance and reproducibility
Governance and Data Sharing Agreements
Data sharing requires governance frameworks that define permitted uses access controls and retention and agreements specify responsibilities for deidentification provenance and for reporting of breaches and institutional review boards and legal teams review projects to ensure compliance with regulations and with participant consent