Meeting Transparency and Traceability Mandates for High-Risk Medical AI

Generating mathematically grounded feature attributions and counterfactual explanations for every diagnosis.

Saliency Maps and Feature Attribution

For every automated chest X-ray diagnosis, Integrated Gradients compute pixel-level importance maps, proving to radiologists that the AI detected true anatomical lesions rather than radiographic marker artifacts.

Immutable Regulatory Decision Logging

Feature importance vectors, raw input tensors, and model version identifiers are signed cryptographically and preserved for 10 years to satisfy EU AI Act Article 12 record-keeping requirements.

Pixel-level attribution proving AI focus on true pathology rather than artifacts
Counterfactual explanation generation demonstrating minimal changes required to alter diagnosis
Tamper-evident cryptographic ledger storing all clinical AI inferences

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