Radiology Triage screens every incoming study the moment it lands in PACS, flags suspected critical findings, and reorders the worklist so the bleed is read before the routine follow-up. Radiologists make every call; the queue just stops being first-come, first-served.
Every study is screened on arrival for suspected critical findings — hemorrhage, pneumothorax, PE, fracture. A positive flag moves the case to the top of the worklist within seconds, not at the next batch read.
Cases are matched to the right reader: subspecialty, credentialing, site and shift. The neuro CT reaches the neuroradiologist on call, and the queue balances itself across the group.
A reader who disagrees with a flag, or with peer review feedback, can request a second opinion from another radiologist. The disagreement gets resolved on record instead of quietly dropped.
Every flag, reorder and override is logged with the model version that made it. When a reviewer asks why a case jumped the queue, the answer is on record — explainable, exportable, retained.
Flags land directly on the worklist your radiologists already read from — no second screen, no separate login. Shown here: the triage queue with a critical flag at the top.
| № | Property | Value | Remark |
|---|---|---|---|
| 01 | Platform | Multi-tenant SaaS | The product. Hosted and shared, so a new site is a login rather than a server — and every tenant gets each improvement as it ships. |
| 02 | Tenancy | Isolated | Your studies, your users and your audit log stay yours. Nothing is pooled across tenants without a written agreement. |
| 03 | Interfaces | DICOM · HL7 | Reads from the PACS and RIS you already run. Results return to the same worklist. |
| 04 | Data | HIPAA | A business associate agreement is signed before any data moves. De-identified for model work. |
| 05 | Models | Versioned | Each release is logged and reversible, and you can see which version made any given call. |
| 06 | Also available | On-premises | Where imaging data cannot leave the network, we scope and build a tailored on-site deployment. |
On-premises and tailored work is scoped as its own engagement and delivered by the team that built the platform, not handed to a third-party integrator.
Every flag carries the region that triggered it and the model version that made the call. A reader sees why before accepting or dismissing it, so the queue order is something you can argue with.
Your studies are not pooled with other tenants’ to train shared models. If we ever want to use your data to improve one, we ask first, in writing, and you can say no and keep the product.
Performance is measured on your own case mix during deployment and reported per site and per model version. We do not carry someone else’s benchmark over to your department.
“I have read studies for twenty-three years, and spent the last few publishing on where AI gets medical imaging wrong. That is exactly why I want it in the worklist — as a second pair of eyes that shows its work.”
Dr. Okur trained in radiology in 2002 and has read across military medical centres, a rehabilitation hospital specialising in neurological and musculoskeletal imaging, and teleradiology. She held research fellowships at Loyola University Medical Center in Chicago and has thirteen peer-reviewed papers in Skeletal Radiology, European Journal of Radiology and Pediatric Radiology, among others. Her 2025 work at CVPR and ETRA examines where generative AI fails in medical imaging, and how a radiologist’s eye movements change when an image is synthetic. She sets the clinical direction for Radiology Triage.
A 30-minute walkthrough with a clinical specialist, on a de-identified copy of your case mix. We will show the flags, the misses, and the audit trail — not just the highlight reel.