Healthcare imaging AI, in plain terms.
The standards and systems behind imaging AI implementation, defined clearly, so every stakeholder is working from the same vocabulary.
Imaging AI implementation
The process of taking a proven imaging AI model and making it fully operational inside a hospital: deployment, integration, workflow embedding, and adoption.
PACS
Picture Archiving and Communication System. The system radiologists use to store, retrieve, and view medical images. Imaging AI must integrate with PACS to be part of the diagnostic workflow.
RIS
Radiology Information System. Manages radiology orders, scheduling, and reporting. AI integration with the RIS keeps results inside the radiologist’s existing process.
EHR
Electronic Health Record. The patient’s longitudinal clinical record. Surfacing imaging AI output in the EHR puts insight where care teams act on it.
DICOM
Digital Imaging and Communications in Medicine. The standard format and protocol for medical images. Imaging AI consumes and produces DICOM to fit clinical pipelines.
HL7
Health Level Seven. A messaging standard for exchanging clinical and administrative data between healthcare systems.
FHIR
Fast Healthcare Interoperability Resources. A modern API-based standard for exchanging healthcare data, increasingly used to connect AI tools to clinical systems.
Last-mile gap
The space between purchasing an AI solution and having it actually deployed, integrated, adopted, and delivering ROI. Where most healthcare AI projects fail.