CDRH Issues Guiding Principles for Transparency of Machine Learning-Enabled Medical Devices
FOR IMMEDIATE RELEASE
June 13, 2024
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 (CDRH)
As artificial intelligence continues to evolve, we are seeing revolutionary opportunities to enhance health care, especially through machine learning. AI can be applied across the spectrum of health applications, including for the prevention, diagnosis, and treatment of a variety of medical conditions, as well as for a range of administrative tasks. Health products, including those that are medical devices, powered by artificial intelligence and machine learning (AI/ML) 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.
The responsible development of AI in health care is a central focus for regulators both in the U.S. and around the globe. In October 2023, President Biden released an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. In January of this year, the U.S. Office of the National Coordinator for Health Information Technology released a final rule on Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-1). A key element of this ongoing discussion is transparency in Machine Learning-enabled Medical Devices (MLMDs). For a MLMD, effective transparency ensures that information that could impact risks and patient outcomes is communicated to all the people who could be interacting with the device, including health care providers, patients, payors, and others, to help make informed decisions.
Today, the FDA, Health Canada, and the U.K.’s Medicines and Healthcare products Regulatory Agency (MHRA) are jointly publishing “Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles.” The underlying objectives of these guiding principles are to foster international harmonization and to underscore the importance of considering transparency throughout the life cycle of MLMDs. Effective transparency ensures that the information delivered to the intended user(s) or audience considers the device’s context of use, as well as the optimal mediums and strategies for successful communication. This information holds the potential to influence the trust of healthcare professionals and patients toward a medical device and inform decisions regarding its use. The comprehensive integration of these guiding principles of transparency across the entirety of the product life cycle serves to ensure that informational requirements are adequately addressed, thereby promoting the safe and effective utilization of MLMDs.
As outlined in the guiding principles document, a comprehensive understanding of users, environments, and workflow is paramount in addressing transparency for MLMDs. Employing human-centered design methods can provide an approach for developing MLMDs with a high degree of transparency.
CDRH is committed to advancing health equity as outlined in our 2022-2025 Strategic Priorities. This joint publication is our third international collaboration on Guiding Principles for AI-enabled devices between the FDA, Health Canada, and the MHRA. Each of these documents demonstrates how the FDA is thinking globally about AI in health care and health equity. Collaborating with international agencies helps to optimize human-centered transparency by supporting predictability and harmonization across jurisdictions. This, in turn, can impact adoption of these devices to advance health care and indirectly encourage the ongoing innovation in the field of AI in health care.
Transparency across health care is an international priority, and the FDA will continue to collaborate with international partners in this area. While the guiding principles presented here are described to promote transparency for MLMDs, transparency is important to consider for all medical devices.
Today's announcement represents another step forward in the FDA's efforts to support responsible innovation and advance patient care.
Additional Resources:
- Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles
- Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles
- Good Machine Learning Practice for Medical Device Development: Guiding Principles
- List of Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices