By exposing and suppressing artifact-driven features in clinical models, you can build systems that generalize better across hospitals and provide human-readable explanations of what clinical concepts drove each prediction.
Clinical AI models often memorize hospital-specific patterns (like note templates) rather than learning true patient health signals, causing them to fail when deployed elsewhere.