Overview
MLOps applies software engineering practices to deploy and maintain AI models reliably in clinical imaging environments. It encompasses version control, automated testing, and reproducible pipelines to ensure consistent performance. Robust MLOps reduces deployment risk and supports regulatory and audit requirements.
Monitoring and Metrics
Continuous monitoring tracks model performance metrics such as sensitivity specificity and calibration across case mix and time. Drift detection and alerting trigger investigations and retraining when performance degrades. Logging inference context and inputs enables root cause analysis for unexpected behavior.
Deployment Practices
Use containerization and orchestration to manage model versions and dependencies across modalities and sites. Implement staged rollouts with shadow mode and A/B testing to validate real world impact before full activation. Maintain rollback procedures and clear ownership for operational incidents.
Governance and Roles
Define roles for data engineers, ML engineers, clinical champions and physicists to share responsibility for model lifecycle. Establish SOPs for validation, incident reporting and retraining thresholds and include these in procurement contracts. Regular audits and transparent documentation support trust and compliance.