Overview

Continual learning enables models to update with new data without forgetting prior knowledge. It supports adaptation to evolving clinical practice and new scanners. This capability reduces need for full retraining.

Techniques

Regularization replay and parameter isolation are strategies to prevent catastrophic forgetting. Incremental learning pipelines incorporate clinician feedback and new annotations. Validation ensures stability and performance.

Clinical Benefits

Continual learning maintains model relevance and improves generalization. It supports rapid incorporation of new disease patterns and protocols. Governance ensures safe updates and rollback mechanisms.

Challenges

Data drift and distribution shifts require monitoring and corrective actions. Versioning and audit trails document model evolution. Regulatory frameworks address continuous learning systems.