Overview
Explainability helps clinicians understand why an AI model produced a given output and supports safe decision making. Audit trails record model version data provenance and inference context to enable retrospective review. Together these elements build trust and support regulatory compliance.
Techniques
Techniques include saliency maps counterfactual explanations and feature attribution to highlight drivers of model outputs. Model cards and datasheets document training data limitations performance metrics and intended use cases. Logging inference inputs outputs and metadata enables reproducible audits.
Clinical Integration
Provide explainability outputs alongside AI results with clear caveats and radiologist interpretation. Use audit logs to investigate unexpected behavior and to support incident reporting. Train clinicians to interpret explainability artifacts and to recognize model limitations.
Governance
Establish policies for acceptable explainability levels and required audit data for each AI class. Include explainability and audit requirements in vendor contracts and procurement. Review explainability performance periodically and update governance as models evolve.