FDA Issues Good Machine Learning Practice Guiding Principles – JD Supra

Guiding Principles

Leveraging Multi-Disciplinary Expertise Throughout the Total Product Life CycleHaving an in-depth understanding of how the ML-enabled medical device will be integrated into the clinical workflow can help ensure that such devices are safe and effective. Developers sh…….

Guiding Principles

  1. Leveraging Multi-Disciplinary Expertise Throughout the Total Product Life Cycle
    Having an in-depth understanding of how the ML-enabled medical device will be integrated into the clinical workflow can help ensure that such devices are safe and effective. Developers should rethink the traditional device development process to include inputs from internal stakeholders such as the chief information security officer, privacy and data strategy personnel, and medical personnel. Input from these stakeholders may be needed earlier in the design and development process than is typical for traditional devices.
  2. Implementing Good Software Engineering, Data Quality Assurance, Data Management and Security Practices
    These practices include methodical risk management and design process designed to capture and communicate design, implementation and risk management decisions and rationale, and to ensure data authenticity and integrity. Developers should also consider FDA’s Content of Premarket Submissions for Management of Cybersecurity in Medical Devices guidance and interoperability of ML-enabled devices within systems or workflows from different manufacturers.
  3. Designing Clinical Studies with Participants and Data Sets That Are Representative of the Intended Patient Population
    Consistent with FDA’s Enhancing the Diversity of Clinical Trial Populations — Eligibility Criteria, Enrollment Practices, and Trial Designs Guidance for Industry (discussed in depth here), data collection protocols should ensure that relevant characteristics of the intended patient population, use and measurement inputs are sufficiently represented in a sample of adequate size in the clinical study or training and test datasets. This allows results and use of data to be generalizable and helps mitigate bias.
  4. Ensuring Training Data Sets Are Independent of Test Sets
    Developers should consider sources of dependence (e.g., patient, data acquisition and site factors) and ensure that training datasets and test datasets are appropriately independent of one another. This principle suggests that regulators will expect developers to explain how they separated the training and test sets to control for bias and confounding factors.
  5. Ensuring Selected Reference Datasets Are Based Upon Best Available Methods
    Developers should use the best available, accepted methods for developing a reference standard to ensure they collect clinically relevant and well-characterized data, and should ensure that they understand the limitations of the reference. Where available, developers should use accepted reference datasets in model development and testing. This may present a hurdle for ML-enabled devices that address disease states or therapeutic areas for which there is no single universally accepted reference standard.
  6. Tailoring Model Design to the Available Data and Reflecting the Intended Use of the Device
    Model design should be suited to the available …….

    Source: https://www.jdsupra.com/legalnews/fda-issues-good-machine-learning-4366380/

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