An organization cannot govern what it cannot list. The model inventory records every AI tool, its owner, version, risk tier, data flows, and monitoring status, and it is the first artifact an inspector asks for.
The AI model inventory is the register of every artificial intelligence tool an organization uses or is evaluating for clinical or operational use. It is the backbone of a governance program because every other control depends on it. Risk tiering, release approval, monitoring, and change control all assume a complete, current list of what is in production.
Radiology workflow orchestration is the layer that decides which AI models run for which studies, deduplicates alerts, resolves priority conflicts, and keeps one audit trail across multiple vendors.
AI product release criteria help health systems and vendors decide whether a clinical AI tool is ready for pilot, go-live, expansion, or re-release after a model or workflow change.
A clinical AI inventory helps health systems know which AI tools are in use, who owns them, what data they touch, what evidence supports them, and what monitoring is required after deployment.
Inventories are usually incomplete on the first pass. AI arrives embedded in EHR modules, imaging platforms, dictation tools, revenue-cycle software, and point solutions bought by a single department. A first inventory should survey clinical and operational leaders directly, review recent contracts for AI terms, and check major platforms for AI features that are enabled but never went through governance.
Each entry has an owner responsible for keeping it current: version changes, monitoring results, use expansions, and retirement. An inventory that is only updated at purchase becomes inaccurate within a release cycle.
The monitoring program reads from it. Regulatory readiness depends on it. Procurement writes to it. In imaging, the orchestration log should reconcile against it so the list matches what is actually running.