A deployed model's launch performance is not its permanent performance. Monitoring and observability track real-world accuracy, drift, override behavior, and downstream workload, and escalate when they move.
Clinical AI monitoring is the practice of checking that a deployed model still does what it was approved to do. Observability is the tooling and data that make monitoring possible: logs of what the model saw, what it returned, how fast, and what the clinician did next. Together they are how a governance program keeps a tool trustworthy between its launch and its retirement.
Radiology AI quality assurance covers local acceptance testing before go-live and ongoing performance monitoring after it: discrepancy review, drift detection, version control, and model management.
Clinical AI governance is usually discussed at the model, vendor, and workflow levels. But hospitals also need to govern the infrastructure below the application layer: data locality, uptime, access, logs, monitoring, recovery, and system change.
Clinical AI monitoring starts after go-live, not before. Health systems need a structured way to watch performance, overrides, workflow burden, safety events, version changes, bias signals, and user trust over time.
In imaging, monitoring overlaps with the department QA program:
See Radiology AI Quality Assurance for the imaging-specific version.