Resolving disagreements between clinicians and algorithmic outputs to drive active learning.
When a board-certified radiologist overrides an AI recommendation, the divergence is scored based on clinical severity and automatically routed to double-blind expert arbitration committees.
Verified clinician corrections are anonymized and queued for scheduled model retraining, ensuring that edge cases and rare pathologies continually improve future algorithm iterations.