Comply · United States

FDA AI/ML SaMD guidance (+ PCCP)

FDA expectations for AI/ML as a medical device.

Who it applies to

Medical-device manufacturers and digital-health companies using AI/ML in products regulated by the FDA.

Key obligations

• Follow Good Machine Learning Practice across the lifecycle
• Define a Predetermined Change Control Plan for adaptive models
• Provide transparency and labeling to users
• Monitor real-world performance and manage model drift

How Maetra maps agents to FDA AI/ML

Maetra provides the inventory, versioned change history, and evidence trail that support lifecycle documentation and post-deployment monitoring for AI/ML models under FDA oversight.

In practice, Maetra:

• Scans and fingerprints each agent. Discover reads the agent’s code — its tools, data access and sensitivity, actions, model, and environment — into an evidence-backed profile tied to the exact file and commit.
• Decides what applies. That profile determines whether FDA AI/ML is in scope for the agent and which of its requirements apply.
• Auto-detects controls and surfaces gaps. Controls your code already satisfies are detected automatically from the scan; the rest become a clear list of gaps, each tied to the requirement and the evidence it still needs.
• Proves it and keeps it current. Close gaps with linked evidence or generated documents — reused across every framework the same control supports — and Maetra re-checks on each rescan and flags evidence that has gone stale.

Related frameworks

Prove FDA AI/ML compliance with Maetra

Classify your AI agents once and Maetra maps them to FDA AI/ML and every other framework it supports — generating the evidence and documentation, tracking gaps and deadlines, and sealing every decision in an immutable audit trail.