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    FDA's Deployment of Agentic AI: What It Means for AI-enabled Diagnostics and SaMD developers

    Shannon Campbell, PhD · November 28, 2025 · 1 min read

    FDAAI/MLAI-SaMDSaMDRegulatory Strategy
    Editorial graphic on FDA's agency-wide deployment of agentic AI and its implications for AI-enabled diagnostics and SaMD developers

    FDA announced agency-wide deployment of agentic AI to support regulatory, scientific, and administrative workflows — with implications for developers of AI-enabled diagnostics.

    In December 2025, the FDA announced agency-wide deployment of agentic AI, advanced systems capable of planning and executing multi-step tasks to support regulatory, scientific, and administrative workflows. While human oversight remains mandatory, these tools are expected to accelerate coordination, improve consistency, and strengthen analytical review across the Agency.

    For developers of AI-enabled diagnostics and Software as a Medical Device (SaMD) this represents a meaningful shift in how efficiently, and how rigorously, FDA can process submissions.

    Key Benefits for Sponsors

    1. Faster Administrative and Review Coordination

    Agentic AI will streamline internal tasks such as meeting scheduling, document comparison, routing of reviews, and pre-market review preparation.

    Benefit: Potential for shorter overall timelines, fewer administrative delays, and more predictable interactions with the Agency.

    2. More Consistent, Data-Driven Reviews

    Agentic AI can highlight cross-document inconsistencies and support internal FDA subject-matter teams with rapid comparisons across prior submissions.

    Benefit: Well-prepared sponsors—those with clean DHF structure, strong traceability, and consistent narratives—will benefit from a smoother review process.

    3. Stronger Lifecycle Alignment for AI/ML Products

    FDA’s expanded use of AI may help normalize expectations around Predetermined Change Control Plans (PCCPs), real-world performance monitoring, and ML lifecycle management.

    Benefit: Clearer guardrails and more transparent expectations for continuous-learning models.

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