Saturday, June 15, 2024

Artificial Intelligence and Machine Learning: FDA Issues New Draft Guidance for Medical Device Software

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The U.S. Food and Drug Administration (FDA) has released new draft guidance aimed at shaping the future of artificial intelligence (AI) and machine learning (ML) in medical device software. This move marks a significant step forward in the regulatory oversight of AI and ML technologies within the healthcare sector.

In April 2023, the FDA issued critical guidance titled “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions.” This guidance provides a framework for manufacturers developing ML-driven device software functions (ML-DSFs), allowing them to iterate, develop, and improve their products within the adaptive nature of AI and ML technologies efficiently.

This strategic initiative is part of the FDA’s ongoing effort, initiated with its 2019 discussion paper and subsequent requests for feedback, to develop a comprehensive premarket review approach for AI/machine learning software modifications. The approach reflects the FDA’s commitment, outlined in their 2021 action plan, to address AI/ML-enabled medical devices in a holistic, collaborative, and multidisciplinary manner.

FDA Introduces Predetermined Change Control Plan for Artificial Intelligence and Machine Learning in Medical Devices

The 2023 guidance introduces the concept of a Predetermined Change Control Plan (PCCP), which allows device manufacturers to proactively specify and seek premarket authorization for intended software modifications. This proactive approach is designed to accommodate the rapid evolution typical of AI and machine learning technologies while ensuring that these changes maintain device safety and effectiveness.

Key components of the PCCP include:

  • Description of Modifications: This should list the specific changes proposed, the rationale for each change, and whether the modification will be implemented automatically or manually.
  • Modification Protocol: Manufacturers must outline the methods for developing, validating, and implementing the modifications. This includes detailing data management practices, ML retraining methods, performance evaluation metrics, machine learning update procedures, and strategies for communicating with users and stakeholders.
  • Impact Assessment: This section requires manufacturers to assess the benefits and risks associated with the modifications, detailing how each change impacts the ML-DSF and the device’s overall functionality, as well as any potential social harm risks and mitigation strategies.

Once a marketing submission with an authorized PCCP is in place, manufacturers can implement the specified modifications following the documented protocols without the need for additional submissions, unless the modification falls outside the scope of the approved PCCP.

Artificial Intelligence and Machine Learning

New Guidance for Artificial Intelligence and Machine Learning Device Modifications

For modifications not covered under the PCCP, manufacturers must submit a new marketing application. This ensures that any significant changes that could affect the safety or effectiveness of the device are rigorously evaluated. The FDA plans to release further guidance on PCCPs to clarify these processes.

The FDA emphasizes that any modifications to AI/ML-DSFs should consider human factors to ensure the continued safety and effectiveness of the device. Changes that affect device labeling or user interaction require careful evaluation to assess potential impacts on user performance and workflow. This new draft guidance not only facilitates the rapid development and deployment of AI/machine learning technologies in medical devices but also ensures these innovations are introduced without compromising safety and efficacy.

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Resource: EMERGO by UL, May 07, 2024

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