Many organizations are willing to pilot imaging AI but stall on whether they need to replace existing systems. In practice, the safer path is often sidecar integration: keep current PACS and workstation habits while embedding prioritization, drafts, and QA trails into the main workflow.
Define success before the model
Before deployment, align on three things: which queues take priority (emergency / screening / outpatient), what counts as an acceptable false positive rate, and how QA will sample results. Without success metrics, even the strongest model remains a demo.
Three key steps for bypass deployment
- Connect via standard DICOM / HL7 / FHIR—avoid custom proprietary protocols.
- Route output to draft plus human confirmation, not silent rewrites of the final report.
- Preserve override and audit trails for QA and teaching review.
Common pitfalls
Standalone web tools break reading rhythm and adoption rates are usually low; missing threshold calibration creates alert noise during peak hours; without cross-department consensus, AI results rarely reach tumor board preparation.