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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 the regulation says
    Regulators distinguish between explainability and interpretability in artificial intelligence (AI) and machine learning (ML), emphasizing their importance for safety and effectiveness. The EU AI Act, for instance, requires high-risk AI systems to be designed with appropriate levels of interpretability, ensuring that their outputs can be understood by humans. Similarly, the FDA’s Good Machine Learning Practice (GMLP) principles advocate for transparency and understanding of ML model behavior, aligning with the need for both explainability and interpretability in MedTech.

    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.

    Examples

    • A diagnostic AI model for radiology provides an explanation, highlighting the specific regions in an image that led to its cancer detection, alongside the confidence score.
    • An AI-powered insulin pump uses an inherently interpretable rules-based system, allowing healthcare providers to easily understand its dosing logic.
    • During a post-market surveillance investigation, an explanation from an AI algorithm helps a manufacturer identify a data bias contributing to inaccurate predictions in a specific patient subgroup.
    Common pitfalls
    • •Confusing explainability as always providing full causal understanding of an AI model, rather than a justification for its output.
    • •Assuming that an interpretable model eliminates the need for thorough validation and clinical risk management.
    • •Failing to tailor the level of explainability or interpretability to the specific clinical application and user needs.
    • •Overlooking the dynamic nature of AI models, where initial explanations may become irrelevant as the model evolves or is re-trained.

    Frequently asked questions

    Explainability helps clinicians understand why an AI model made a particular decision, fostering trust and enabling informed clinical judgment. It is crucial for incident investigation and continuous improvement.
    Grouped by theme

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    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.