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

Note: From an article entitled, "Good Machine Learning Practice for Medical Device Development: Guiding Principles." 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).

Source: U.S. Food and Drug Administration, October 27, 2021

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