Explainable AI provides feature attributions, counterfactuals and human readable rationales for model predictions. Black box models can hinder adoption; explainability techniques help clinicians understand drivers of risk scores and recommendations. Techniques include SHAP values, attention maps for images, and rule extraction to present transparent reasoning.
Explainability improves clinician trust and supports regulatory and ethical requirements for clinical AI. Approaches that reveal model reasoning to support clinician interpretation and accountability. Present explanations at appropriate granularity, validate explanations with clinicians, and avoid misleading simplifications.
Main Points: Explainable AI in Clinical Use | Feature attribution | Counterfactuals | Visual explanations | Rule extraction | Clinician validation
Quick Facts: Explainability aids trust but can be misinterpreted | Multiple methods may be needed | Clinician validation is essential | Explanations must be actionable | Regulatory expectations are evolving
Topics related to Explainable AI in Clinical Use include interpretability | trust | regulation