Artificial intelligence (AI) and machine learning (ML) present unique ethical, security, and technical considerations. Insights gained by the development of high-quality medical devices that use AI/ML have the potential to transform healthcare. A joint effort by the U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom’s (UK) Medicines and Healthcare products Regulatory Agency (MHRA) has identified 10 guiding principles intended to establish the foundation of GMLP for medical device development. The 10 guiding principles identify areas where collaborative bodies could work to advance GMLP and include:
- 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
Read the details of the guiding principles or download a PDF version of principles on the official FDA website.
Good Machine Learning Practice for Medical Device Development: Guiding Principles
Source: FDA The identified guiding principles can inform the development of good machine learning practices to promote safe, effective, and high-quality medical devices.
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