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 discussionExternal Link Disclaimer 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.
CDRH Issues Guiding Principles for Transparency of Machine Learning-Enabled Medical Devices | FDA
CDRH Issues Guiding Principles for Transparency for MLMDs
The FDA, Health Canada, and MHRA jointly issued the Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles.
www.fda.gov
