Draft Guidance for AI-Enabled Device Software Functions: Key Highlights

The FDA's draft guidance provides a comprehensive framework for the development, submission, and lifecycle management of AI-enabled device software functions (AI-DSFs). Aimed at industry and FDA staff, it emphasizes a Total Product Life Cycle (TPLC) approach to ensure safe, effective, and compliant AI-enabled medical devices.

Key Elements of the Guidance

  • Total Product Life Cycle (TPLC) Approach
    • Focuses on the entire lifecycle of AI-enabled devices, from development to post-market surveillance.
  • Addressing Transparency and Bias
    • Stresses the importance of transparency and strategies to mitigate bias in machine learning models.
  • Alignment with Consensus Standards
    • Recommends incorporating software-related consensus standards and including supporting documentation in submissions with a Declaration of Conformity to recognized standards.
  • Risk-Based Approach
    • Encourages sponsors to adopt a risk-based approach for testing and recommendations in marketing submissions.
  • Predetermined Change Control Plan
    • Proposes the use of a Change Control Plan to manage planned modifications to AI-DSFs, ensuring compliance while enabling flexibility for updates.
  • FDA Research and Experience
    • Draws on FDA's expertise in machine learning, deep learning, and neural networks, alongside relevant FDA guidance documents and regulations.
  • Submission Best Practices
    • Advises sponsors to consult other relevant FDA resources and guidance documents for seamless marketing submissions.

Objective

The guidance aims to support industry compliance with FDA recommendations for AI-enabled devices, fostering innovation while prioritizing patient safety and device efficacy.

This document is a critical step toward creating a robust regulatory framework for AI in healthcare.

Source: https://www.fda.gov/media/184856/download

Region: United States of America

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