Recon AI
Themes it raises
The comment as filed
Re: Considerations for the Regulation of Generative AI-Enabled Medical Devices — Discussion Paper and Request for Feedback
Recon.AI appreciates the FDA’s opportunity to provide feedback on the regulatory considerations surrounding generative AI-enabled medical devices.
We offer the following recommendations from the perspective of an independent AI governance and operational-evidence technology developer. Recon.AI is not a medical-device manufacturer, and these recommendations should not be interpreted as claims of clinical validation or regulatory certification.
1. Distinguish documented controls from operating controls.
A manufacturer’s description of human oversight, safety checks, monitoring, or escalation procedures does not independently establish that those controls operated during a consequential AI interaction.
FDA should consider encouraging structured, inspectable operational evidence demonstrating when a control was invoked, whether it passed or failed, what action followed, and whether human intervention was required.
2. Support continuous assurance throughout the device lifecycle.
Generative AI-enabled devices may experience material changes in model behavior, operating context, dependencies, or intended use.
Lifecycle monitoring should address not only model performance but also the continuing effectiveness of governance controls, including authorization boundaries, human oversight, escalation, and intervention mechanisms.
3. Establish evidence expectations for consequential AI actions.
Where generative AI systems initiate recommendations, invoke tools, or influence consequential workflows, evidence records should enable reviewers to reconstruct:
* What the system attempted to do.
* What authority and operating constraints applied.
* What information supported the action.
* Which controls executed and their outcomes.
* Whether human approval was required and obtained.
* Whether an exception, intervention, or recovery occurred.
These records should be proportionate to device risk and designed to protect patient privacy.
4. Evaluate human oversight as an operating mechanism.
Human-in-the-loop requirements should be assessed through evidence of actual review, approval, escalation, and intervention rather than relying solely on documented workflow design.
Evaluation should also consider approval fatigue, alert burden, delayed intervention, and whether oversight remains effective as systems evolve.
5. Address third-party monitoring and independent verification.
FDA should consider how independent assurance providers can contribute evidence about governance-control execution without replacing manufacturers’ responsibilities or qualified clinical evaluation.
Third-party evidence should identify its scope, provenance, integrity protections, limitations, and the distinction between observed control execution and conclusions about clinical safety or regulatory compliance.
6. Include recovery and rollback evidence.
When material changes, unexpected behavior, or control failures occur, lifecycle evidence should document detection, escalation, containment, rollback or recovery decisions, responsible human authority, and verification of the resulting system state.
Recommended regulatory direction
Recon.AI encourages FDA to consider risk-proportionate, interoperable evidence expectations for continuous governance of generative AI-enabled medical devices.
Such an approach could improve the inspectability of operating controls while preserving manufacturer accountability, clinical evaluation requirements, and FDA’s regulatory authority.
The central distinction is straightforward: evidence that a governance control was designed is not equivalent to evidence that it operated, and evidence that it operated does not by itself establish that a medical device is safe or effective.
Founder & CEO, ReconAI.net