Five specialties.
One inference engine.
Hover any card to see a real clinical finding Diagnose surfaced — and the outcome it changed.
Full-body CT, MRI, and X-ray analysis with sub-millimeter precision
Diagnosed 3mm GGO in right upper lobe — missed on initial attending read
Patient enrolled in surveillance protocol 6 weeks earlier
Echo, cardiac MRI, and coronary CT angiography analysis
Non-obstructive CAD flagged via late gadolinium enhancement pattern
Intervention scheduled within 48 hours
Tumor segmentation, staging, and treatment response tracking
Sub-centimeter peritoneal deposits identified — negative on PET
Stage upstaged; systemic therapy initiated
Whole-slide image analysis for biopsy and cytology interpretation
AI delineated tumor margins with 0.2mm precision on frozen section
Re-excision avoided; OR time saved by 45 minutes
Brain MRI, stroke detection, and white matter analysis
Large vessel occlusion detected at triage, pre-CT perfusion
Door-to-needle time reduced by 22 minutes
40 data points before
you scroll to the CTA.
CTPA reads completed in 0.4s with 98.6% sensitivity — vs. 22-minute average radiologist read time
Standalone AI outperformed average radiologist by 11.5% AUC on 91,000-mammogram validation set
ICH flagged and radiologist notified in under 1 second — mean alert-to-read time: 3.2 minutes
AI identified LVEF <40% on routine ECG — 6 weeks prior to symptomatic presentation in 83% of cases
Gleason score agreement with expert consensus: 96.4% — surpassing inter-pathologist concordance of 87%
Opportunistic osteoporosis screening on routine chest CT — 340% increase in fracture detection vs. no AI
Memorial Health System reduced unread study queue from 4,200 to 180 studies within 72 hours of deployment
Adenoma detection rate improved from 31% to 47% per-colonoscopy — statistically significant across 12,000 procedures
From the people who read the scans.

"We had 4,200 unread studies when we went live. Seventy-two hours later, the queue was 180. I've never seen a deployment move that fast or that cleanly.
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