FOR IMMEDIATE RELEASE
Oct 24, 2023
The following is attributed to Troy Tazbaz, Director of the Digital Health Center of Excellence in the FDA’s Center for Devices and Radiological Health
Health products powered by artificial intelligence and machine learning (AI/ML) are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots to algorithms for detecting cancer in mammography and interpretations of chest x-rays. Part of the promise of these products is that they use software algorithms that can potentially 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.
Recently, the FDA issued a draft guidance proposing Predetermined Change Control Plans, or PCCPs, as a way to ensure that AI/ML-enabled medical devices can be safely, effectively, and rapidly modified, updated, and improved in response to new data. The growing adoption of new software tools and digital platforms that incorporate machine learning to meet patient needs also highlights the importance of promoting an internationally harmonized approach to PCCPs for AI/ML-enabled devices.
Today, the FDA, Health Canada, and the U.K.’s Medicines and Healthcare products Regulatory Agency (MHRA) are jointly publishing “Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles.” These guiding principles are intended to lay the foundation for PCCPs for Machine Learning Enabled Medical Devices and encourage international harmonization.
Collaborating with international agencies helps to optimize the benefits of PCCPs by supporting predictability and harmonizing regulatory considerations across jurisdictions. Collaboration also encourages stakeholder consensus on the core concepts of PCCPs. Ultimately, this consensus helps put safe and effective advancements in the hands of health care providers and users faster, increasing the pace of medical device innovation in the United States and enabling more personalized medicine.
According to the guiding principles document, the foundational characteristics of PCCPs relate to being focused, risk-based, and evidence-based, as well as having a high degree of transparency and considering total product lifecycle management. These key characteristics help to support safe and effective implementation of PCCPs for machine learning-enabled medical devices.
CDRH is committed to advancing health equity as outlined in our 2022-2025 Strategic Priorities. This joint publication is a continuation of the international collaboration between the FDA, Health Canada, and the MHRA on good machine learning practices (GMLP) guiding principles. Both of these documents build on efforts outlined in the FDA’s AI/ML action plan and demonstrate how the FDA is thinking globally about health equity. Collaborating with the FDA’s international colleagues to advance equity is important, because AI/ML-enabled devices are developed and deployed globally.
Furthermore, this document complements the FDA’s recent efforts related to the amendment of the Federal Food, Drug, and Cosmetic Act by the Food and Drug Omnibus Reform Act (FDORA) of 2022, providing the FDA with express authority to authorize PCCPs, including the FDA’s draft guidance on PCCPs in AI/ML issued in April. While these Guiding Principles consider specifically the best practices for PCCPs for AI/ML-enabled medical devices, these principles may be helpful to consider for the application of PCCPs more broadly.
Today’s announcement is an important step toward advancing safe and effective medical device innovations for the benefit of public health.
- Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles
- Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions
- Good Machine Learning Practice for Medical Device Development: Guiding Principles
- Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan
- Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback
- Artificial Intelligence and Machine Learning in Software as a Medical Device
- Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices