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HIPAA-compliant Data Module
A tested starting point for hipaa-compliant data module reduced repeated setup work.
We built a practical HealthTech solution combining on-prem dicom data collection, quantized inference system, and AI-powered prioritization workflow. The project delivered 4.5x Throughput Lift and 40ms Edge Inference.
Trusted by teams building ambitious products
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 automatically localizing and prioritizing time-critical anomalies.
A practical solution designed around the client’s existing tools, teams, and day-to-day workflow.
The client relied on a shared 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 biggest issues were diagnostic lag, privacy constraints, and analyst fatigue. The team needed a faster, clearer way to manage the work while keeping the right checks in place.
Radiologists view images in chronological order, regardless of severity.
AI screens every slice in live use use use use use use as the scan completes, prioritizing high-risk cases.
Cloud processing was non-viable due to Patient health information 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.
Custom model quantization techniques reduced the compute footprint by 70% while preserving sub-millimeter localization accuracy.
Embedded local scrubber ensures data is pseudonymized before internal diagnostic logging, meeting strict HIPAA Title II requirements.
The system doesnt just detect', it automatically negotiates with the RIS (Radiology Info System) to re-prioritize the human queue.
Tested foundations helped the team spend less time on setup and more time on the parts that made this product useful.
A tested starting point for hipaa-compliant data module reduced repeated setup work.
Reusable work for dicom streamer module let the team focus more time on the client’s specific needs.
This made it easier to add diagnostic monitoring platform without rebuilding common foundations.
A tested starting point for edge reliability controls reduced repeated setup work.
A straightforward before-and-after view of what changed for the team and their customers.
Quantized ViT models achieved diagnostic consistency with senior radiologists on initial localization work.
AI-powered 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.
Coretus didnt just build a detector, they built a live diagnostic partner . We eliminated the Cloud-Gap' completely, allowing our radiologists to focus onInterpretation while the Edge platform handles the prioritization with surgical accuracy.