MedTech Terms
    The authoritative reference
    All terms

    Algorithmic Bias and Fairness

    Systematic differences in model performance across demographic or clinical subgroups.

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

    Definition

    Algorithmic bias occurs when a model performs differently across subgroups (e.g., by sex, race, age, comorbidity) due to training data imbalance, label noise, or proxy variables. Fairness analyses quantify and mitigate these gaps.
    What the regulation says
    Regulators emphasize fair and unbiased performance for AI/ML-based MedTech. The FDA

    What this means in practice

    FDA's GMLP principles call for representative datasets and subgroup performance analysis. EU AI Act high-risk requirements explicitly address bias monitoring across the lifecycle.

    Examples

    • A diagnostic AI trained predominantly on data from one ethnic group performs poorly when used on patients from other ethnic groups, leading to misdiagnoses.
    • A risk stratification algorithm for disease progression overestimates risk for one sex due to proxy variables correlated with socioeconomic status in the training data.
    • A wearable device's algorithm, designed to detect a medical condition, shows lower accuracy for individuals with darker skin tones because the training images were not diverse enough.
    Common pitfalls
    • •Failing to define fairness metrics relevant to the specific clinical context and patient population.
    • •Overlooking the potential for bias to be introduced or amplified at various stages of the AI/ML lifecycle, from data acquisition to deployment.
    • •Assuming that a globally accurate model is also fair across all demographic subgroups.

    Frequently asked questions

    Algorithmic bias most commonly stems from unrepresentative or imbalanced training datasets, where certain patient subgroups are underrepresented or poorly labeled.

    Cross-references

    Part of

    A larger framework or document this term belongs to.

    Grouped by theme

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    FDA/Health Canada/MHRA·1IMDRF·1MDCG·1
    1. 1
      Good Machine Learning Practice
      Verified
      FDA/Health Canada/MHRAfda.gov
    2. 2
      IMDRF - Software as a Medical Device
      Verified
      IMDRFimdrf.org
    3. 3
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu

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