Service

AI in Radiology Workflows

Getting imaging AI out of the pilot and into daily clinical use — evaluated against real cases, not vendor benchmarks, for health systems nationwide.

Most AI pilots die in the gap between demo and deployment

The model works fine in the vendor's demo environment. Then it hits your actual PACS, your actual HL7 feed, your actual radiologist worklist — and the integration falls apart long before anyone gets to evaluate whether the AI is any good. We close that gap.

What we do

Viewer & worklist integration — overlay results and priority flags directly into the radiologist's existing viewer, without a second window to babysit.
Inference pipeline engineering — routing studies to inference endpoints, handling retries and failures gracefully, and getting structured results back into PACS/RIS.
Clinical validation design — evaluation frameworks that test the model against your case mix, not a curated benchmark set.
Vendor evaluation support — independent technical review of AI vendors' integration claims before you sign anything.

We're vendor-agnostic on purpose. Our job is to make sure the AI tool you choose actually earns its place in the workflow — not to sell you one.

Why it matters

A triage AI that flags studies correctly but adds 40 seconds of latency to every study won't survive contact with a busy reading room. Workflow engineering is what separates AI that gets used from AI that gets quietly turned off.