Overview
AI governance ensures safety efficacy and fairness for clinical imaging AI. Institutions must validate models locally and monitor post deployment performance. Governance covers data provenance bias testing and clinician oversight.
Validation Practices
Validation requires diverse datasets external testing and prospective clinical evaluation. Performance drift monitoring and retraining plans are essential. Explainability and failure mode analysis support clinician trust.
Operational Integration
Integrate AI into PACS/RIS workflows with clear triage and escalation rules. Radiologist oversight remains mandatory for diagnostic decisions. Logging and audit trails enable regulatory compliance.
Ethics and Policy
Address bias fairness and patient consent for secondary data use. Establish multidisciplinary committees for approval and monitoring. Document decisions and maintain transparent vendor agreements.