Data governance defines stewardship, access controls, provenance tracking and quality standards for clinical datasets.  High quality labeled data with provenance supports reproducible model development and auditability.  Governance frameworks include data catalogs, deidentification standards, consent management and role based access.

Strong governance underpins trustworthy AI and protects patient privacy while enabling innovation.  Structures and policies to manage clinical data for safe and ethical AI use.  Establish governance committees, document data lineage, and enforce policies for reuse and sharing to maintain trust and compliance.

Main Points: Data Governance for Clinical AI | Provenance tracking | Access controls | Deidentification | Consent management | Quality metrics

Quick Facts: Provenance supports reproducibility | Access controls protect privacy | Deidentification reduces reidentification risk | Consent management respects patient preferences | Quality metrics guide dataset fitness

Topics related to Data Governance for Clinical AI include privacy | provenance | consent