Most imaging AI integration problems are predictable. Answer these questions before you deploy and you remove the surprises that stall projects after go-live.
DICOM routing and your PACS
- Can the AI receive studies over standard DICOM and route results back to the same study?
- How are outputs represented: secondary capture, structured report, or overlays?
- Will findings appear on the radiologist's existing worklist, not a separate viewer?
- How are prior studies and comparisons handled?
HL7 messaging into the RIS
- Which order and result messages (ORM, ORU) does the workflow depend on?
- Are message formats and segments mapped to your RIS's actual configuration?
- How are failures, retries, and acknowledgements handled?
FHIR resources and the EHR
- Which FHIR resources carry the output into the EHR (DiagnosticReport, Observation, ImagingStudy)?
- Is SMART on FHIR / OAuth authentication supported by your EHR?
- Where does the result surface for the care team that acts on it?
Workflow embedding
- Does the AI run automatically on the right studies, or require manual triggering?
- How many clicks from "study opened" to "insight visible"?
- Can radiologists accept, edit, or dismiss results inside their normal reading flow?
Security and infrastructure
- On-premise or cloud, and does it match your security posture and data-residency rules?
- How is PHI protected in transit and at rest?
- What are the uptime, monitoring, and support expectations?
Before you sign off
If you cannot answer these confidently, the integration is not ready, and the time to find that out is before deployment, not after. A short discovery that maps your current state against this checklist is usually the fastest route to a deployment that actually lands.