Artificial intelligence (AI)- and machine learning (ML)-based technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Example high-value applications include earlier disease detection, more accurate diagnosis, identification of new observations or patterns on human physiology, and development of personalized diagnostics and therapeutics. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance. The ability for AI/ML software to learn from real-world feedback (training) and improve its performance (adaptation) makes these technologies uniquely situated among software as a medical device (SaMD) and a rapidly expanding area of research and development. Our vision is that with appropriately tailored regulatory oversight, AI/ML-based SaMD will deliver safe and effective software functionality that improves the quality of care that patients receive.
This discussion paper proposes a framework for modifications to AI/ML-based SaMD that is based on the internationally harmonized International Medical Device Regulators Forum (IMDRF) risk categorization principles, FDA’s benefit-risk framework, risk management principles in the software modifications guidance, and the organization-based TPLC approach as envisioned in the Digital Health Software Precertification (Pre-Cert) Program. It also leverages practices from our current premarket programs, including the 510(k), De Novo, and PMA pathways.
This discussion paper describes an innovative approach that may require additional statutory authority to implement fully. The proposed framework is being issued for discussion purposes only and is not a draft guidance. This document is not intended to communicate FDA's proposed (or final) regulatory expectations but is instead meant to seek early input from groups and individuals outside the Agency prior to development of a draft guidance.