AI Governance for Radiology AI: Workflow, Monitoring, and FDA Requirements

What FDA expectations mean for radiology AI governance

For radiology AI, FDA SaMD expectations translate into an operational governance discipline after deployment. Governance leaders need to understand which AI-enabled imaging tools are in scope, how each tool is used, which model version is active, and how clinical use remains aligned with the validated and cleared intended use.

The governance model should connect regulatory classification with routine operational controls. That includes validated model use, predetermined change controls, runtime monitoring, workflow checkpoints, version traceability, and post-market evidence. These controls help ensure that AI systems used in radiology can be monitored, controlled, investigated, and documented without creating avoidable friction for radiologists.

Radiology AI governance control plane

A radiology AI governance control plane helps organize the responsibilities that sit between regulatory expectations and day-to-day clinical workflow. The supplied governance model focuses on three areas: regulatory scope, runtime monitoring, and clinical workflow fit.

Runtime monitoring capabilities needed for deployed radiology AI

Runtime monitoring is a central part of radiology AI governance because model behavior must remain observable after deployment. The governance program should be able to track how AI-enabled imaging software is used, which version produced a result, and whether the model is operating within expected boundaries.

Monitoring should support investigation and documentation. When an output, workflow event, or performance signal requires review, governance leaders need enough context to understand what happened, which model version was involved, and whether the event remains consistent with validated model use and approved change-control boundaries.

• Track model versions associated with deployed radiology AI use.
• Preserve audit trails for inputs, outputs, confidence signals, and relevant workflow events.
• Monitor performance indicators that may reveal drift or unsafe behavior.
• Support investigation when deployed model behavior needs review.
• Connect monitoring evidence to post-market documentation and audit readiness.

Embedding governance into clinical imaging workflows

Radiology AI governance should fit into clinical imaging workflows rather than operate as a separate, disconnected compliance process. Governance checks need to be placed around the radiologist workflow in a way that preserves review efficiency and avoids unnecessary interpretation delay.

In practice, this means governance should account for the systems and workflows radiology teams already use, including PACS, RIS, and radiologist review workflows. Controls are most useful when they provide traceability, policy enforcement, monitoring, and documentation while remaining aligned with clinical operations.

1. 1

   Define the regulatory and operational scope

   Map each AI-enabled imaging tool to its SaMD classification, cleared intended use, and applicable change-control boundaries.

2. 2

   Monitor the model at runtime

   Track inputs, outputs, confidence signals, model versions, and performance indicators to detect drift or unsafe behavior.

3. 3

   Keep controls close to clinical workflow

   Place governance checks around radiologist workflow without introducing avoidable interpretation delay.

Change control, audit trails, and documentation

Change control, audit trails, and documentation are necessary for making radiology AI governance durable over time. Predetermined change controls help define the boundaries for model updates and operational changes. Audit trails make it possible to reconstruct what happened during use. Documentation connects the operational record to governance review and post-market evidence.

For governance leaders, these capabilities help demonstrate that deployed radiology AI remains controlled after implementation. They also support practical investigation when model behavior, workflow events, or monitoring indicators need additional review.

Evaluation checklist for radiology AI governance platforms

When evaluating a governance platform for radiology AI, prioritize capabilities that make deployed models observable, traceable, and controllable in routine clinical use.

• Ability to connect each deployed model to regulatory scope, validated use, and cleared intended use.
• Version traceability for every deployed model and relevant workflow event.
• Runtime monitoring for inputs, outputs, confidence signals, and performance indicators.
• Workflow checkpoints that fit normal PACS, RIS, and radiologist review processes.
• Audit logging that supports investigation, documentation, and post-market evidence.
• Policy enforcement and runtime controls for enterprise AI deployments.