A clinical AI that clinicians actually trusted.

Radiologists were drowning in report generation.

Post-scan, radiologists at partner hospitals were spending hours structuring narrative findings into reports that downstream clinicians, billing, and QA all needed. Existing AI assistants were fast — and ignored. The assumption was that speed was the win. Discovery told a different story.

20+ clinical interviews, 15+ hospital systems.

We ran structured interviews with radiologists, referring clinicians, and QA leads. The pattern was unambiguous: they didn't trust opaque outputs. A model that suggested a finding without showing why got dismissed. A model 15% slower but that surfaced source imagery regions, cited prior scans, and flagged uncertainty got adopted.

We pivoted the MVP. Interpretability first, speed second. The product spec changed in week four — and that was the week the project started working.

Build for adoption, not for demo day.

The rebuilt MVP shipped with: region-level evidence highlighting, confidence intervals per finding, one-click model-feedback capture, and an audit log aligned to FDA SaMD requirements. We drove NLP accuracy from 68% to 88% through structured feedback loops — radiologists flagged errors inline; those corrections flowed back into prompt tuning and edge-case handling within the sprint.

$500K+ ARR in six months. 4,000+ manual hours eliminated annually.

The platform launched across the client's partner network. ARR surpassed $500K within six months — faster than the original velocity-first spec projected, because adoption was the constraint, not feature count. Downstream, the structured data pipeline we designed eliminated 4,000+ manual hours per year of post-processing across partner sites.