About
Validation proves performance at one point in time. Governance requires proving it over time.
As clinical AI scales across vendors, versions, and even in-house models, institutions face a new reality: performance can drift, regulatory expectations are rising, and accountability remains internal. Vendor dashboards and validation studies alone are not sufficient to demonstrate continuous oversight.
This executive webinar explores what it actually takes to operationalize post-deployment monitoring in real clinical environments. Through institutional experience and governance discussion, we will examine how infrastructure, not reporting, enables defensible AI oversight across the lifecycle.
If your organization is deploying or scaling radiology AI, this session will help you assess whether your current approach can withstand version changes, performance variability, and regulatory scrutiny.
Agenda
The Post-Go-Live Gap
Understand why validation studies and vendor dashboards are insufficient once AI is live, and how performance drift, version changes, and workflow variability create governance blind spots.
When Monitoring Becomes Accountability
See why post-market surveillance is an executive responsibility, not an operational feature, and how longitudinal, version-level evidence changes institutional decision-making.
What Continuous Oversight Requires
Learn how event-level performance capture, harmonized output schemas, and independent audit trails enable reproducible, institution-owned evidence across commercial and in-house AI solutions.
How Infrastructure Integrates into Real Clinical Environments
Explore how continuous monitoring connects with existing PACS and RIS systems, preserves data sovereignty, and defines governance roles across IT and radiology.
From Compliance to Defensible Portfolio Decisions
Understand how emerging regulatory expectations reshape monitoring requirements and how infrastructure-based oversight supports decisions to scale, upgrade, pause, or replace AI tools.
