MedTech Terms
    The authoritative reference
    All terms

    Good Machine Learning Practice

    Guiding principles for the development of AI/ML-enabled medical devices.

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

    Definition

    Good Machine Learning Practice (GMLP) describes guiding principles - jointly issued by FDA, Health Canada, and the UK MHRA - for developing AI/ML-enabled medical devices that are safe, effective, and high-quality.
    What the regulation says
    The FDA, Health Canada, and the UK MHRA jointly developed Good Machine Learning Practice (GMLP) principles to guide the development of artificial intelligence/machine learning (AI/ML) enabled medical devices. These principles focus on ensuring the safety, effectiveness, and quality of these devices throughout their lifecycle. GMLP helps inform regulatory expectations, particularly for aspects like data management, model validation, and the performance of human-AI teams.

    What this means in practice

    GMLP principles inform FDA review expectations, including data management, model validation, and human-AI team performance.

    Examples

    • A medical device manufacturer implements a comprehensive data governance framework to ensure the quality and unbiased nature of the training data used for its AI-powered diagnostic tool, aligning with GMLP principles for data management.
    • During the validation of an AI/ML algorithm for detecting anomalies in medical images, a company performs extensive testing across diverse patient populations to demonstrate robust performance and minimize algorithmic bias, as recommended by GMLP.
    • A change control process is established for an adaptive AI/ML-enabled insulin pump to manage algorithm updates and monitor real-world performance, ensuring consistency with GMLP expectations for continuous learning systems.
    Common pitfalls
    • •A common pitfall is failing to establish a robust data management plan that addresses data quality, integrity, and representativeness, potentially leading to biased or inaccurate model performance.
    • •Misinterpreting GMLP as a static checklist rather than a set of guiding principles can lead to superficial compliance without true assurance of device safety and effectiveness.
    • •Underestimating the importance of real-world performance monitoring and continuous learning for adaptive AI/ML models can result in undetected performance degradation over time.
    • •Neglecting to involve multi-disciplinary teams including clinicians, data scientists, and regulatory experts throughout the device development process can lead to overlooked risks and inefficiencies.
    • •Failing to adequately document the rationale for model choices, data curation, and validation strategies can hinder regulatory review and post-market surveillance.

    Frequently asked questions

    The primary goal of GMLP is to ensure that AI/ML-enabled medical devices are developed in a manner that guarantees their safety, effectiveness, and high quality throughout their lifecycle.

    Cross-references

    Governs

    Things this term applies rules or requirements to.

    Contains

    Sub-elements or required artefacts of this term.

    Grouped by theme

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-06-20
    1. 1
      GMLP Guiding Principles
      Verified
      FDAfda.gov
    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.