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Recent strides in artificial intelligence (AI) and machine learning technology is opening the door to the development of increasingly smart medical devices capable of turning big data and digital information into actionable insights for healthcare professionals. AI is having a significant impact on the evolution of smart devices and how healthcare will be delivered in the future.

AI, coupled with machine learning, is calling into question what it means to approve a medical device capable of “learning” and improving its capabilities after its initial approval for use. As a result, the FDA is considering a total product lifecycle-based regulatory framework for these technologies that would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device is maintained. For Medical device manufacturers considering medical regulatory submissions for devices utilizing AI and machine learning, this should be of concern.

What is artificial intelligence and how is it transforming medical devices?

Artificial intelligence is the basis for machine learning. It is the digital capability of a device, system, or application to be trained to accomplish specific tasks by processing large amounts of data, recognizing patterns in that data, and using “if-then” algorithms to reach conclusions without direct human input.

In healthcare, AI is being used to analyze relationships between prevention or treatment techniques and patient outcomes. AI is becoming a key tool for developing diagnostic processes and treatment protocols, as well as for personalized medicine and patient monitoring and care. What makes AI and machine learning technologies different from other medical device software is their potential to adapt and optimize device performance in real-time to continuously improve health care for patients.

Traditionally, the FDA reviews medical devices through an appropriate premarket pathway, such as premarket clearance (510(k)), De Novo classification, or premarket approval (PMA). The FDA may also review and clear modifications to medical devices, including software as a medical device, depending on the significance or risk posed to patients of that modification.

The FDA’s traditional paradigm of medical device regulation was not designed for adaptive artificial intelligence and machine learning technologies. Under the FDA’s current approach to software modifications, the FDA anticipates that many of these artificial intelligence and machine learning-driven software changes to a device, as well as quality system development, may need a premarket review.

In January of this year, the FDA revealed its new test plan for the next phase of its digital health Pre-Certification (PreCert) pilot program. The focus of PreCert version 1.0 will be to establish processes for software as a medical device (SaMD), which may include software functions that use AI and machine learning algorithms, within FDA’s current authorities. The agency indicated additional authority may be required prior to fully implementing the PreCert program for other types of digital health tools, rather than just first-of-its-kind SaMD.

On April 2, the FDA published a discussion paper “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback” that describes the FDA’s foundation for a potential approach to premarket review for artificial intelligence and machine learning-driven software modifications.

The paper discusses practices from the FDA’s current premarket programs and outlines ideas that rely on IMDRF’s risk categorization principles, the FDA’s benefit-risk framework, risk management principles described in the software modifications guidance, and the organization-based total product lifecycle approach (also envisioned in the Digital Health Software Precertification (Pre-Cert) Program).

At this time, the FDA is expecting a commitment from manufacturers on transparency and real-world performance monitoring for artificial intelligence and machine learning-based software as a medical device, as well as periodic updates to the FDA on what changes were implemented as part of the approved pre-specifications and the algorithm change protocol.

The proposed regulatory framework could enable the FDA and manufacturers to evaluate and monitor a software product from its premarket development to post-market performance. This potential framework allows for the FDA’s regulatory oversight to embrace the iterative improvement power of artificial intelligence and machine learning-based software as a medical device, while assuring patient safety.

For medical device consulting, including regulatory submissions,  and medical regulatory consulting regarding how new FDA regulatory practices regarding AI and machine learning may affect your product approval, contact the regulatory compliance experts here at MEDIcept.

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