AI-Assisted Mammography Detection Rollout for a Women's Imaging Network
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Problem
Growing 3D screening backlog
A 16-centre women's imaging network was falling behind on 3D mammography screening. Each exam produces hundreds of image slices, reading times run well above 2D studies, and a radiologist shortage had pushed the unread backlog past three weeks, delaying results letters and follow-up scheduling. Cases were read strictly in arrival order, regardless of suspicion level.
Solution
AI-prioritised reading workflow
We integrated a regulator-cleared AI detection engine into the network's reading workflow. Case-level suspicion scores feed a prioritised worklist so likely-abnormal exams surface first, finding overlays render inside the existing viewer with mandatory radiologist sign-off, and a network-wide dashboard tracks turnaround, recall rates and reading volumes across all 16 centres.
Measurable Impact
What changed after launch
Screening report turnaround reduced from 11 days to 4 across the network
Average radiologist reading time per 3D screening exam cut by 24%
Three-week unread exam backlog cleared within 9 weeks of full rollout
Recall rate held within 0.4 percentage points of baseline under quarterly audit
Tech & Tools Used
What powered the build
Ready to Build your Medical Imaging & Diagnostics Business with Visual Detection & Diagnostics
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