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    Adversarial Robustness

    Resilience of an ML model to inputs deliberately crafted to cause misclassification.

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

    Definition

    Adversarial examples - small, often imperceptible perturbations - can flip medical-imaging predictions. Robustness evaluation, input sanitization, and detection of out-of-distribution inputs are emerging expectations for safety-critical AI.

    What this means in practice

    Particularly relevant for AI radiology and pathology devices where image acquisition is partially under attacker influence.

    Primary references

    3 sources
    Link health: 2 verified 1 needs review· last checked 2026-05-09
    NIST·1FDA·1IMDRF·1
    1. 1
      NIST AI 100-2
      Needs review
      NISTcsrc.nist.gov
    2. 2
      FDA - AI/ML-Enabled Medical Devices
      Verified
      FDAfda.gov
    3. 3
      IMDRF - Software as a Medical Device
      Verified
      IMDRFimdrf.org

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