In recent years, the FDA has embarked on an ongoing journey to develop a premarket review approach for artificial intelligence (AI)/machine learning (ML) software modifications. This journey included the Agency’s 2019 discussion paper and request for feedback on the proposed regulatory framework, several workshops to gather inputs from various stakeholders, as well as the Agency’s action plan released in 2021 that described the FDA’s strategy for addressing AI/ML-enabled medical devices in a holistic, collaborative, and multidisciplinary manner. In April 2023, the US Food and Drug Administration (FDA) released the guidance, Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions. Furthermore, in March of 2024, the Center for Biologics Evaluation and Research (CBER), Center for Drug Evaluation and Research (CDER), Center for Devices and Radiological Health (CDRH), and Office of Combination Products (OCP) published a brief paper “which outlines the agency's commitment and cross-center collaboration to protect public health while fostering responsible and ethical medical product innovation through Artificial Intelligence.”
This article will look more closely at the April 2023 guidance and some of its direct impacts on AI/ML product development, and particularly ML-enabled device software functions (ML-DSFs) which the guidance focuses on.
Controlling the iterative nature of AI/ML
ML is a powerful tool that allows software to learn through real-world data to augment a device’s functionality. ML-enabled technologies represent a transformative shift in healthcare providing solutions with the ability to assist healthcare providers and allow for better patient outcomes. ML is greatly characterized by its adaptability and its capability to improve its performance through iterative input from the manufacturer, or from the ML itself (i.e., the software’s functionality). As such, ML-device software functions (ML-DSFs) present challenges for traditional regulatory pathways (i.e., 510(k) premarket clearance, De Novo classification, premarket approval) intended for devices that are developed and released as a final, “static” form. As AI and ML continue to transform medical devices and healthcare, FDA’s new draft guidance attempts to accommodate the “adaptive” nature of these AI/ML technologies and “proposes a least burdensome approach to support iterative improvement through modifications to an ML-enabled device software functions (ML-DSFs) while continuing to provide a reasonable assurance of device safety and effectiveness”.
FDA Draft Guidance on Predetermined Change Control Plans for Artificial Intelligence/Machine Learning-Enabled Medical Devices
Guidance for manufacturers developing ML-DSF-enabled devices to rapidly iterate, develop and improve under the highly adaptive nature of AI and ML.
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