MHRA issues principles for Good Machine Learning Practice
The Medicines and Healthcare products Regulatory Agency, along with the US Food and Drug Administration and Health Canada, have jointly identified 10 guiding principles that can inform the development of Good Machine Learning Practice (GMLP).
The principles come at the same time as pharmacy IT systems such as Titan have started using AI and machine learning (AI/ML) to build data on how pharmacists carry out their clinical checks.
AI/ML technologies have the potential to transform health care by deriving important insights from the vast amount of data generated during the delivery of health care every day. Apps with a medical function are required to be registered with the MHRA as medical devices.
The 10 guiding principles will help promote safe, effective and high-quality medical devices that use artificial intelligence and machine learning, says the MHRA. They 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.