Engineering an on-device Edge AI mesh to localize anomalies in high-res DICOM streams—achieving sub-40ms inference latency while ensuring zero PHI leaves the local facility perimeter.
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High-volume multi-specialty hospital processing 2,000+ DICOM imaging studies daily.
AI Architect + Computer Vision Engineer + HIPAA Compliance Lead embedded with Radiology Ops.
Reducing radiologist alert fatigue by autonomously localizing and prioritizing time-critical anomalies.
Local Triton Inference Servers, Quantized Vision Transformers (ViT), and HL7/FHIR event-mesh.
The client relied on a centralized PACS (Picture Archiving and Communication System) that required radiologists to manually review every slice of high-resolution CT and MRI scans. During peak hours, the diagnostic backlog reached 18 hours, delaying critical interventions.
The 'Execution Gap' was structural: cloud-based AI solutions were rejected due to high-latency DICOM uploads and strict hospital policies against sending PHI to external servers. The enterprise required a 'Compute-at-the-Source' model that worked within the surgical theater's real-time constraints.
Radiologists view images in chronological order, regardless of severity.
AI screens every slice in real-time as the scan completes, prioritizing high-risk cases.
Cloud processing was non-viable due to PHI transit risks and data residency laws.
Inference and storage occur within the hospital's local network (HIPAA-locked).
Wait times between imaging completion and first radiologist look were systemic.
Localized tumor bounding boxes are available before the patient leaves the modality.
Proprietary model quantization techniques reduced the compute footprint by 70% while preserving sub-millimeter localization accuracy.
Embedded local scrubber ensures metadata is pseudonymized before internal diagnostic logging, meeting strict HIPAA Title II requirements.
The system doesn't just 'detect'—it autonomously negotiates with the RIS (Radiology Info System) to re-prioritize the human queue.
Pre-audited local storage and encryption modules designed for PHI data-at-rest in edge environments.
Production-ready connectors for low-latency image ingestion from GE, Siemens, and Philips modalities.
Real-time monitoring for model drift and 'false negative' distribution audits for clinical safety.
Automated failover logic ensuring the AI system defaults to 'Radiologist-Only' mode if hardware parity fails.
Quantized ViT models achieved diagnostic parity with senior radiologists on initial localization work.
Agentic prioritization reduced the wait time for 'highly probable' severe cases by over 80%.
Local edge deployment ensured diagnostics remained active even during hospital-wide internet outages.
Client Testimonial
Coretus didn't just build a detector—they engineered a real-time diagnostic partner. We eliminated the 'Cloud-Gap' completely, allowing our radiologists to focus onInterpretation while the Edge mesh handles the triage with surgical precision.
Chief of Radiology // Tier-1 Research Hospital