FDA Introduces Principles for Good Machine Learning Practice

According to a release from the U.S. Food and Drug Administration (FDA), the 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).

The release states that “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 healthcare 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.”

That said, “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.”

The 10 guiding principles are:

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 datasets are representative of the intended patient population
4. Training datasets are independent of test sets
5. Selected reference datasets are based upon the 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 essential information that is clear
10. Deployed models are monitored for performance and retraining risks are managed

The release states that the guiding principles should be used to adopt good practices that have been proven in other sectors, tailor practices from other sectors so they apply to medical technology and the healthcare sector, and create new practices specific for medical technology and the healthcare sector.

The release concludes that “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.”