Fact Based List:
FDA: 10 Guiding Principles for Developing Good Machine Learning Practice
Submitted by Charlene Ice on Tue, 11/16/2021 - 13:43
- Multi-Disciplinary Expertise Is Leveraged Throughout the Total Product Life Cycle.
- Good Software Engineering and Security Practices Are Implemented.
- Clinical Study Participants and Data Sets Are Representative of the Intended Patient Population.
- Training Data Sets Are Independent of Test Sets.
- Selected Reference Datasets Are Based Upon Best Available Methods.
- Model Design Is Tailored to the Available Data and Reflects the Intended Use of the Device.
- Focus Is Placed on the Performance of the Human-AI Team.
- Testing Demonstrates Device Performance during Clinically Relevant Conditions.
- Users Are Provided Clear, Essential Information.
- Deployed Models Are Monitored for Performance and Re-training Risks are Managed.
Notes: 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
Source URL: https://www.fda.gov/medical-devices/software-medical-device-...
List Ratings: |
Lists You Might Also Be Interested In
- Kelly Gooch: 6 Things to Know About HHS Asst. Dr. Charmaine
- Top Ten States with Highest Percentages of Uninsured Residents 2010
- Castlight Health: Variation in Survival Rates for High-Risk Procedures Across Different Hospitals
- Cleveland Clinic: Top 10 Medical Innovations 2017
- Vitals 5 Health Care Predictions for 2015 (and Beyond)
Login or register to post comments