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    Locked vs. Adaptive Algorithm

    Distinction between models whose behavior is fixed at release vs. those that continue learning.

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

    Definition

    A locked algorithm produces the same output for the same input every time after release. An adaptive algorithm changes its behavior over time as it is exposed to new data, either continuously or in scheduled retraining cycles.
    What the regulation says
    The FDA's guidance, 'Good Machine Learning Practice for Medical Device Development' and the 'Predetermined Change Control Plan' (PCCP) framework, differentiates between locked and adaptive algorithms. For locked algorithms, regulatory review focuses on the algorithm's performance at the time of submission. For adaptive algorithms, regulators assess the manufacturer's control over the modification protocol, the 'Algorithm Change Protocol,' to ensure predictable and safe evolution.

    What this means in practice

    FDA's PCCP framework lets manufacturers prospectively define and validate the boundaries of allowable adaptive changes so retraining does not require a new submission for each model update.

    Examples

    • A diagnostic AI that learns to identify new patterns in medical images as it processes more patient scans is an adaptive algorithm requiring a PCCP.
    • An insulin pump algorithm that adjusts dosing recommendations based on a patient's continuously acquired glucose data and activity levels is an adaptive algorithm.
    • A risk stratification tool that uses a fixed set of patient demographics and historical health records to predict disease likelihood is a locked algorithm.
    Common pitfalls
    • •Failing to establish clear performance bounds and risk mitigations for adaptive algorithm changes can lead to regulatory non-compliance.
    • •Treating an adaptive algorithm as a locked algorithm for validation purposes will result in insufficient oversight of its evolving behavior.
    • •Not documenting the data used for training and retraining, along with corresponding version control, can create significant auditability issues.
    • •Assuming all adaptive algorithm changes are minor and do not impact safety or effectiveness can lead to patient harm and regulatory scrutiny.

    Frequently asked questions

    Yes, an adaptive algorithm might be 'locked' at a specific version after extensive validation, especially if its environment stabilizes or further adaptation introduces undue risk. This transition typically requires clear documentation and justification.

    Cross-references

    Uses

    Concepts or artefacts this term builds on.

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    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    MDCG·1FDA·2
    1. 1
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu
    2. 2
      FDA - Software as a Medical Device (SaMD)
      Verified
      FDAfda.gov
    3. 3
      FDA - AI/ML-Enabled Medical Devices
      Verified
      FDAfda.gov

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