The first AI radiology reading engine to ship at clinical scale.
01 — Studies worklist, priority tiers, multi-site
02 — DICOM viewer, voice transcript, report editor
03 — ARRG generates the structured report
04 — RadEnhance catches what the AI missed
05 — Mobile companion for radiologists on call
01/05
Role
Senior Product Designer
Timeline
May 2024 - Present
Reports Generated
1M+ reports generated
The Engine
From a DICOM scan to a signed report
Internally we called it ARRG. It takes a DICOM scan, generates a complete structured radiology report, and hands it to the radiologist to review, refine, and sign. The reading-to-report loop that used to take hours now runs in minutes.
I led design end to end, partnering with the CEO (a practicing physician) and engineering across every surface: the viewer, the AI output, the report templates, and the editing controls.
The product had to serve radiologists across the US, Qatar, and UAE, each with different regulatory requirements, while maintaining HIPAA compliance and integrating with existing PACS infrastructure.
The Workspace
Where the AI output lands in the radiologist's day
Generating an accurate report is half the problem. The other half is delivering it inside a workflow radiologists already trust. The reporting workspace pairs the AI transcript with a familiar template structure and the worklist they manage their day from. The AI integrates without breaking habit.
Outcomes
1M+
Reports generated on the platform
1,000+
Clinicians using RadioView.AI™
40%
Reduction in charting time
Deployed across radiology practices in the US, Qatar, and UAE.
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