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    Explainability and Interpretability

    Methods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction.

    Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 5, 2026

    Definition

    Explainability provides post-hoc rationalizations of model decisions; interpretability is a property of inherently transparent models. Both feed clinician trust and inform Human Factors design.

    What this means in practice

    EU AI Act high-risk requirements and FDA Good Machine Learning Practice both expect manufacturers to address explainability proportionate to clinical risk.

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-05-09
    FDA·2MDCG·1
    1. 1
      FDA GMLP
      Verified
      FDAfda.gov
    2. 2
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu
    3. 3
      FDA - Software as a Medical Device (SaMD)
      Verified
      FDAfda.gov

    Inline markers like [1] jump to the matching reference above.