AI assists. Radiologists retain final interpretation.
Reduce worklist burden and reading variability with AI-assisted triage, structured summaries, and reporting automation that respects your judgment.
What we hear from radiology groups
Worklist burden
Studies stack up; cognitive load grows across modalities and shifts.
Reading variability
Inter-reader variability harms quality programs and peer-review metrics.
Reporting overhead
Structured reporting and key-image management add friction.
Cognitive fatigue
Long sessions degrade attention; AI assistance helps consistency hold.
Less queue management. More interpretation.
AI sits behind the worklist and the report — never in front of the radiologist.
Triage in the background
AI flags potentially urgent or suspicious cases as studies arrive. The worklist reorders; the radiologist stays in flow.
Read with overlay context
Candidate findings, density, and measurements are presented in-context — and dismissable in one click.
Draft, edit, sign
Structured drafts with key images speed sign-out. Every edit and override is captured, never silently overwritten.
Feedback feeds QA
Override patterns surface to peer review and continuous-improvement loops — not a hidden ML metric.
Decision support that earns its keep.
Lower worklist time-to-read
Override visibility
Faster structured reporting
Radiology group second-read pilot (in progress)
Radiology group second-read pilot (in progress)
Radiology group second-read pilot (in progress)
A multi-state radiology group is piloting AI-assisted screening mammography second-read with full override capture. We'll publish methodology and metrics once peer review concludes.