The 21st Century Cures Act, enacted in December 2016, amended the definition of “medical device” in section 201(h) of the Federal Food, Drug, and Cosmetic Act (FDCA) to exclude five distinct categories of software or digital health products. In response, the US Food and Drug Administration (FDA) issued new digital health guidance and revised several
In response to the rapid pace of innovation in the health and life sciences arena, the US Food and Drug Administration (FDA) is taking a proactive, risk-based approach to regulating digital health products. Software applications and other transformative technologies, such as artificial intelligence and 3D printing, are reshaping how medical devices are developed, and FDA is seeking to align its mission and regulatory obligations with those changes.
FDA’s digital health software precertification program is a prime example of this approach. Once fully implemented, this voluntary program should expedite the path to market for software as a medical device (SaMD), and promote greater transparency between FDA and regulated entities.
Under the program, FDA will conduct a holistic review of the company producing the SaMD, taking into account aspects such as management culture, quality systems and cybersecurity protocols, to ascertain whether the company has developed sufficient infrastructure to ensure that its products will comply with FDA requirements and function safely as intended. Companies that fulfill the requirements of the excellence appraisal and related reviews will receive precertification that may provide for faster premarket reviews and more flexible approaches to data submissions at the outset.
In April 2019, the US Food and Drug Administration (FDA) issued a white paper, “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device,” announcing steps to consider a new regulatory framework to promote the development of safe and effective medical devices that use advanced AI algorithms. AI, and specifically ML, are “techniques used to design and train software algorithms to learn from and act on data.” FDA’s proposed approach would allow modifications to algorithms to be made from real-world learning and adaptation that accommodates the iterative nature of AI products while ensuring FDA’s standards for safety and effectiveness are maintained.
Under the existing framework, a premarket submission (i.e., a 510(k)) would be required if the AI/ML software modification significantly affects device performance or the device’s safety and effectiveness; the modification is to the device’s intended use; or the modification introduces a major change to the software as a medical device (SaMD) algorithm. In the case of a PMA-approved SaMD, a PMA supplement would be required for changes that affect safety or effectiveness. FDA noted that adaptive AI/ML technologies require a new total product lifecycle (TPLC) regulatory approach and focuses on three types of modifications to AI/ML-based SaMD:
Throughout 2017, the health care and life sciences industries experienced a widespread proliferation of digital health innovation that presents challenges to traditional notions of health care delivery and payment as well as product research, development and commercialization for both long-standing and new stakeholders. At the same time, lawmakers and regulators made meaningful progress toward modernizing…