Real-world monitoring is part of the operating problem
Open-ended or adaptive AI outputs can change with data, use, and underlying models. Premarket tests alone may not capture how the product behaves in live care.
After launch, postmarket AI performance and real-world monitoring stay hard. Development and validation are not the finish line when the product touches patients, clinicians, and consequential workflows.
GNS-AI works on strategy, evidence, data, workflow integration, real-world performance, monitoring, governance, and Decision Control for medical-device and digital-health organizations.
FDA has published a discussion paper on considerations for regulating generative AI-enabled medical devices. The paper seeks feedback on risk assessment, premarket evaluation, and postmarket monitoring. It is discussion material, not final guidance. Sponsors still need practical ways to watch real-world performance after authorization.
Open-ended or adaptive AI outputs can change with data, use, and underlying models. Premarket tests alone may not capture how the product behaves in live care.
Teams need methods to detect degradation, re-check competence, and manage change without losing accountability.
Product, clinical, quality, and CTO leaders need a clear path from monitoring findings to operating decisions.
From SaMD and clinical decision support to remote monitoring and complaints. Decision Control applies when AI shapes a consequential downstream action, not as a blanket layer over the entire product.
Products where AI output can influence clinical or operational action.
Evidence, workflow fit, and real-world performance after clearance or approval.
Targeting review, user reliance, and consequence when recommendations matter.
Data quality, alerting burden, and operating economics in live use.
Complaint and signal work tied to product performance and change control.
Case-level control when AI-influenced actions remain consequential in use.
Public entry points for product, clinical, quality, and technology leaders.
Five business days to pressure-test one device or digital-health initiative.
See Initiative ReviewBounded implementation when requirements are clear enough to deliver.
See Scoped BuildScoped. Prove real-world efficacy before broader deployment.
See Pilot Evaluation3-4 weeks · Starts at $30,000 · Typical $30,000-$45,000.
See DCAPersistent runtime Decision Control. Scoped.
See DCPBring the monitoring, workflow, or control question. We will recommend the next engagement.