Imaging AI is often discussed as a standalone algorithm. In a hospital, its usefulness depends on the wider imaging environment: how studies are acquired, stored, routed, viewed and connected to a patient record.
Think beyond radiology
A patient’s diagnostic story may include radiology, digital pathology, cardiology and other image-rich specialties. An enterprise imaging approach brings these assets into a governed architecture. DICOM and interoperability research offer a practical foundation for exchanging studies and AI results across systems. The image’s path from capture to clinical decision matters as much as the model itself.
Make the archive useful
A vendor-neutral archive can support access across departments and systems, but the archive alone does not create a good workflow. Consistent identifiers, metadata, access permissions, prior studies and reliable links to the EHR all matter. Procurement should consider portability and what happens when applications or AI models change.
Ask operational questions
Where will AI results appear? How are worklists prioritized? Can a specialist compare the output with prior studies and another specialty’s findings? Who monitors failed analyses and model version changes? These questions connect technical architecture to care delivery.