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FDA Issues Artificial Intelligence/Machine Learning Action Plan

On January 12, 2021, the US Food and Drug Administration (FDA) released its Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. The Action Plan outlines five actions that FDA intends to take to further its oversight of AI/ML-based SaMD:

  1. Further develop the proposed regulatory framework, including through draft guidance on a predetermined change control plan for “learning” ML algorithms
    • FDA intends to publish the draft guidance on the predetermined change control plan in 2021 in order to clarify expectations for SaMD Pre-Specifications (SPS), which explain what “aspects the manufacturer changes through learning,” and Algorithm Change Protocol (ACP), which explains how the “algorithm will learn and change while remaining safe and effective.” The draft guidance will focus on what should be included in an SPS and ACP in order to ensure safety and effectiveness of the AI/ML SaMD algorithms. Other areas of focus include identification of modifications appropriate under the framework and the submission and review process.
  2. Support development of good machine learning practices (GMLP) to evaluate and improve ML algorithms
    • GMLPs are critical in guiding product development and oversight of AI/ML products. FDA has developed relationships with several communities, including the Institute of Electrical and Electronics Engineers P2801 Artificial Intelligence Medical Device Working Group, the International Organization for Standardization/ Joint Technical Committee 1/ SubCommittee 42 (ISO/ IEC JTC 1/SC 42) – Artificial Intelligence, and the Association for the Advancement of Medical Instrumentation/British Standards Institution Initiative on AI in medical technology. FDA is focused on working with these communities to come to a consensus on GMLP requirements.
  3. Foster a patient-centered approach, including transparency
    • FDA would like to increase patient education to ensure that users have important information about the benefits, risks and limitations of AI/ML products. To that end, FDA held a Patient Engagement Advisory meeting in October 2020, and the agency will use input gathered during the meeting to help identify types of information that it will recommend manufacturers include in AI/ML labeling to foster education and promote transparency.
  4. Develop methods to evaluate and improve ML algorithms
    • To address potential racial, ethical or socio-economic bias that may be inadvertently introduced into AI/ML systems that are trained using data from historical datasets, FDA intends to collaborate with researchers to improve methodologies for the identification and elimination of bias, and to improve the algorithms’ robustness to adapt to varying clinical inputs and conditions.
  5. Advance real world performance monitoring pilots
    • FDA states that gathering real world performance data on the use of the SaMD is an important risk-mitigation tool, as it may allow manufacturers to understand how their products are being used, how they can be improved, and what safety or usability concerns manufacturers need to address. To provide clarity and direction related to real world performance data, FDA supports the piloting of real world performance monitoring. FDA will develop a framework for gathering, validating and evaluating relevant real world performance parameters [...]

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To Market, To Market: FDA’s Digital Health Precertification Program

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.

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Reviewing Key Principles from FDA’s Artificial Intelligence White Paper

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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Digital Health Year in Review: 2017 Trends and Looking Ahead to 2018

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 the existing legal framework in a way that will both adequately protect patients and consumers and support and encourage continued innovation, but their efforts have not kept pace with what has become the light speed of innovation. As a result, some obstacles, misalignment and ambiguity remain.

We are pleased to bring you this review of key developments that shaped digital health in 2017, along with planning considerations and predictions for the digital health frontier in the year ahead.

Read the full Special Report.




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