Regulatory Explainability

A quantitative metric that evaluates whether an explanation accurately reflects the model's true reasoning process. It measures the correlation between attributed feature importance and the actual change in model output when those features are perturbed or occluded.

• Perturbation-based: Measures output drop when top-k attributed pixels are removed
• Comprehensiveness: Quantifies if the explanation captures all necessary features
• Sufficiency: Verifies if the explanation alone is enough for the prediction
• Essential for proving clinical validity to notified bodies

The dangerous false sense of security arising from a plausible-looking but unfaithful saliency map. A heatmap may highlight anatomically convincing regions while masking the model's reliance on spurious correlations or confounding artifacts.

• Can be induced by attribution attacks that manipulate explanations
• Detected through systematic ablation studies and faithfulness testing
• A key risk flagged in FDA's Good Machine Learning Practice (GMLP)
• Mitigated by using multiple orthogonal explanation methods (e.g., SHAP + Grad-CAM)

Explanation methods that incorporate anatomical priors and clinical knowledge to ensure saliency maps are physiologically plausible. This prevents models from highlighting irrelevant regions like imaging table edges or patient clothing.

• Uses anatomical atlases as spatial priors for brain, lung, and cardiac imaging
• Explanation regularization penalizes activations outside known organ boundaries
• Aligns with radiomics feature extraction standards
• Critical for demonstrating lesion attribution to regulatory reviewers

Regulatory frameworks mandate that explanation methods must be applied without modifying the underlying diagnostic model's architecture or training process. This preserves the frozen, validated state of the cleared algorithm.

• Grad-CAM and Integrated Gradients operate on trained weights
• Avoids the need for concept bottleneck models that require retraining
• Supports change control documentation for SaMD submissions
• Enables retrospective explainability on legacy deployed models

A secure, chronological record of all inputs, outputs, and explanations generated by a Software as a Medical Device (SaMD). This tamper-proof log is essential for post-market surveillance and regulatory review, capturing the exact saliency map, prediction, and model version used for a specific patient at a specific time. It provides the forensic evidence needed to reconstruct and validate every clinical AI decision, ensuring accountability under FDA and MDR frameworks.

A quantitative metric that evaluates whether an explanation accurately reflects the model's true reasoning process. It measures the correlation between attributed importance scores and the actual change in model output when features are perturbed. A high faithfulness score is critical for regulatory approval, as it proves that a saliency map is not an interpretability illusion but a genuine representation of the model's decision logic, directly supporting safety and efficacy claims.

A human-AI collaboration paradigm where a medical professional actively reviews and interprets AI-generated explanations before making a final diagnosis. This workflow is a cornerstone of regulatory strategy, positioning the AI as a decision support tool rather than an autonomous agent. By keeping the clinician responsible for the final judgment, this approach aligns with current FDA guidelines for Computer-Aided Detection (CADe) and diagnostic software, mitigating liability and safety risks.

The process of aligning a clinician's subjective trust in an AI tool with its objective performance. Effective explainability prevents both automation bias (over-reliance) and algorithm aversion (under-reliance). Regulatory bodies are increasingly concerned with this human factors aspect, as a miscalibrated clinician can negate the safety benefits of an accurate AI. Explanations must be designed to foster an appropriate level of scrutiny, directly impacting the risk management file required for MDR compliance.

A malicious manipulation of an input image designed to cause a model to produce a specific, incorrect explanation while maintaining its original, correct classification. This adversarial technique undermines the very purpose of regulatory explainability by creating a false sense of security. Demonstrating adversarial robustness for both predictions and their explanations is becoming a critical component of safety assurance for SaMD, proving the system's reliability against deliberate attempts to deceive auditors.

Saliency maps constrained by prior anatomical knowledge to ensure explanations are physiologically plausible. For regulatory acceptance, a heatmap highlighting a diagnosis must align with established medical understanding—a tumor classification should be based on the lesion, not a confounding artifact. Techniques that integrate anatomical atlases or organ segmentations into the explanation process provide this clinical grounding, transforming a mathematical attribution into a medically meaningful and auditable justification for a diagnostic decision.