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  5. Good Machine Learning Practice for Medical Device Development: Guiding Principles
  1. Software as a Medical Device (SaMD)

Good Machine Learning Practice for Medical Device Development: Guiding Principles

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The U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA) have jointly identified 10 guiding principles that can inform the development of Good Machine Learning Practice (GMLP). These guiding principles will help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning (AI/ML).

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. They use software algorithms to learn from real-world use and in some situations may use this information to improve the product’s performance. But they also present unique considerations due to their complexity and the iterative and data-driven nature of their development.

These 10 guiding principles are intended to lay the foundation for developing Good Machine Learning Practice that addresses the unique nature of these products. They will also help cultivate future growth in this rapidly progressing field.

The 10 guiding principles identify areas where the International Medical Device Regulators Forum (IMDRF), international standards organizations, and other collaborative bodies could work to advance GMLP. Areas of collaboration include research, creating educational tools and resources, international harmonization, and consensus standards, which may help inform regulatory policies and regulatory guidelines.

We envision these guiding principles may be used to:

  • Adopt good practices that have been proven in other sectors
  • Tailor practices from other sectors so they are applicable to medical technology and the health care sector
  • Create new practices specific for medical technology and the health care sector

As the AI/ML medical device field evolves, so too must GMLP best practice and consensus standards. Strong partnerships with our international public health partners will be crucial if we are to empower stakeholders to advance responsible innovations in this area. Thus, we expect this initial collaborative work can inform our broader international engagements, including with the IMDRF.

We welcome your continued feedback through the public docket (FDA-2019-N-1185) at Regulations.gov, and we look forward to engaging with you on these efforts. The Digital Health Center of Excellence is spearheading this work for the FDA. Contact us directly at Digitalhealth@fda.hhs.gov, software@mhra.gov.uk, and mddpolicy-politiquesdim@hc-sc.gc.ca.

Guiding Principles

  1. Multi-Disciplinary Expertise Is Leveraged Throughout the Total Product Life Cycle
  2. Good Software Engineering and Security Practices Are Implemented
  3. Clinical Study Participants and Data Sets Are Representative of the Intended Patient Population
  4. Training Data Sets Are Independent of Test Sets
  5. Selected Reference Datasets Are Based Upon Best Available Methods
  6. Model Design Is Tailored to the Available Data and Reflects the Intended Use of the Device
  7. Focus Is Placed on the Performance of the Human-AI Team
  8. Testing Demonstrates Device Performance during Clinically Relevant Conditions
  9. Users Are Provided Clear, Essential Information
  10. Deployed Models Are Monitored for Performance and Re-training Risks are Managed
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