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:


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As digital health innovation continues to move at light speed, both new and incumbent stakeholders find themselves on a new frontier—one that challenges traditional health care delivery and payment frameworks, in addition to changing the landscape for product research, development and commercialization. Modernization of the existing legal framework has not kept pace with the rate of digital health innovation, leaving no shortage of obstacles, misalignment and ambiguity for those in the wake.

What did we learn in 2017 and what’s to come on the digital health frontier in the year ahead? From advances and investments in artificial intelligence (AI) and machine learning (ML) to the increasingly complex conversion of health care innovation and policy, McDermott’s Digital Health Year in Review details the key developments that shaped digital health in 2017, along with planning considerations and predictions for the health care and life science industries in 2018. 
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Although the incorporation of technology into human endeavours—commercial, political and personal—is a normal component of technological innovation, the advent of artificial intelligence technology is producing significant challenges we have not felt or understood with earlier innovations. For many years, for example, there has been speculation, research and public debate about the impact of the internet,