Multi-Modality Image Analysis
Diagnosify's core engine processes DICOM images directly from your PACS, applying modality-specific deep learning models that have been trained and validated on millions of annotated studies.
- Supports X-Ray, CT, MRI, and whole-slide pathology formats
- Processes full study volumes in under 8 seconds on standard GPU hardware
- Outputs structured findings reports in HL7 FHIR-compatible format
- Confidence scoring and uncertainty quantification on every finding
Adaptive Learning Architecture
The Diagnosify model adapts to institutional reading patterns and patient population demographics through federated learning, improving performance over time without transmitting patient data off-site.
- Federated learning preserves patient privacy — no PHI leaves your network
- Continuous model improvement validated against radiologist ground truth
- Drift detection and automated retraining triggers
- Audit trail for every model update and decision
Radiologist Workflow Integration
Diagnosify is designed as a second-reader tool, not a replacement. Findings surface directly within your existing PACS viewer through zero-footprint overlays, structured reports, and priority worklist sorting.
- Zero-footprint PACS integration via DICOMweb and DICOM SR
- Priority worklist reordering based on detection confidence
- One-click finding acceptance or override with documented reason
- Structured reporting templates aligned to RadLex and ACR standards
