MedTech Terms
    The authoritative reference
    Back to AI/ML Devices Deep Dive
    Software & AIAI / ML in DevicesSoftware Lifecycle

    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 September 19, 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. Common techniques include saliency maps for images, feature attribution methods such as SHAP, and example-based explanations. Explanations can mislead if they look plausible but do not reflect what the model actually used, so they should be validated like any other output. Regulators focus on transparency for users: what the device does, its performance and limitations, and the information users need to judge when to rely on it.
    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.

    Related terms

    Grouped by theme
    Cited by

    Where this term appears across MedTech Terms.

    Learning paths (1)

    Latest in MedTech

    Sources

    3 sources

    Every citation below opens the original document. Each is graded against our source-tier hierarchy so you can see what rests on binding law versus commentary.

    Tier 2Regulator guidance and consensus· 3
    Link health: 3 verified· last checked 2026-06-20
    FDA·2MDCG·1
    1. 1
      FDA GMLP
      Tier 2 Verified
      FDAfda.gov
    2. 2
      MDCG Software Guidance
      Tier 2 Verified
      MDCGhealth.ec.europa.eu
    3. 3
      FDA - Software as a Medical Device (SaMD)
      Tier 2 Verified
      FDAfda.gov

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