Neural-network differential diagnosis on DICOM imaging. Annotated findings, ranked by confidence, delivered before the radiologist's first note.
DIAGNOSTIC PIPELINE
Three steps. One breath.
The platform mirrors the radiologist's own workflow — receive, read, report — except the first pass happens in 4.2 seconds on average.
PHASE 01
UPLOAD
Drop the DICOM. Nothing else required.
De-identified DICOM files upload directly to our HIPAA-compliant ingest pipeline. No PACS reconfiguration. No middleware. The system validates metadata, confirms series completeness, and queues the study in under 3 seconds.
Drop DICOM file here
.dcm · .dicom · Series ZIP
PHASE 02
ANALYZE
The neural network reads every pixel.
Our ensemble model — trained on 4.2 million annotated studies — segments anatomy, identifies anomalies, and assigns ICD-10 codes with confidence percentages. Bounding boxes, heatmaps, and layered overlays appear as the algorithm builds its differential.
Model Ensemble
7 networks
Training Studies
4.2M
Pathologies
128 classes
Avg Latency
4.2s
PHASE 03
REPORT
A structured report, ready to sign.
Ranked differential diagnoses with ICD-10 codes, confidence scores, and exportable annotations. One click sends to your PACS, RIS, or EMR. The radiologist reviews, annotates, and signs — the AI has already done the first pass.
Upload a de-identified DICOM and receive a real annotated result in the sandbox.
DIFFERENTIAL DIAGNOSIS REPORT
Chest CT · Series 001
Ready to see it on a real scan?
CLINICAL VALIDATION
Numbers a department head can take to the C-suite.
Prospective validation across 14 institutions, 340,000 studies, peer-reviewed in Radiology and NEJM AI.
Sensitivity on pneumonia detection
vs 91.2% radiologist baseline
Median time to first differential
across 128 pathology classes
Studies processed in production
across 34 health systems
Critical findings missed in validation
FDA 510(k) cleared dataset
"At 2am when I'm the only radiologist covering three hospitals, Diagnose is the second opinion I can't get from a colleague. It flags what I need to see first."
Dr. Sarah Chen, MD
Attending Radiologist
Regional Medical Center, Boston MA
"We evaluated six AI imaging vendors. Diagnose was the only one that could demonstrate HL7 FHIR integration in our test environment before we signed anything."
Marcus Webb
Director of Clinical IT
St. Augustine Health Network
"The department chair asked me to justify the budget. I showed her the sensitivity numbers and the 4-second latency demo. She approved it in the same meeting."
Dr. Priya Anand, MD
Chief of Radiology
University Hospital System, Chicago IL
INTEGRATION ARCHITECTURE
No rip-and-replace. No six-month implementation.
Diagnose sits alongside your existing PACS as a passive listener. Average time from signed contract to first annotated result: 14 days.
SYSTEM ARCHITECTURE · SIMPLIFIED
Your PACS
Existing infrastructure unchanged
Diagnose Gateway
DICOM listener + HL7 broker
AI Engine
Neural ensemble inference
Your Workflow
Annotated results returned
PACS
EMR / EHR
Standards
Infrastructure
FOR HOSPITAL IT DIRECTORS
Need architecture docs before the call?
Download our integration specification sheet — HL7 message schemas, DICOM conformance statement, security architecture, and deployment topology diagrams.
PDF · 24 pages · No form required
SANDBOX ENVIRONMENT
Try a Sample Scan. Right now.
Upload any de-identified DICOM chest CT or X-ray. The neural network returns annotated findings in seconds. No account required for the sandbox.
Drop DICOM here
.dcm · .dicom · Series ZIP
Or try our sample chest CT
WHAT YOU RECEIVE
Annotated DICOM overlay
Bounding boxes + heatmaps on your scan
Differential diagnosis report
ICD-10 codes ranked by confidence
Confidence breakdown
Per-finding probability scores
Exportable PDF
Ready for chart attachment or review
Prefer a guided demo with your own imaging data?
No commitment · 30-min session · Your DICOM environment