Case Studies › ImaginiHealth
Healthtech & Digital Health

From Research Models to Clinical-Grade Radiology Platform

ImaginiHealth had powerful AI models trained on millions of medical images but no production platform to deploy them. Techdots built the end-to-end system — DICOM ingestion, AI inference pipeline, radiologist workstation UI, and clinician reporting tools — turning research-stage technology into a HIPAA-compliant product used in live clinical settings.

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I
ImaginiHealth
Healthtech & Digital Health
58%
Reduction in average radiology read time
3.2x
More scans processed per radiologist per shift
94%
AI finding accuracy rate validated in clinical trials
20 weeks
timeline
This engagement is best for
Healthtech startups with trained ML models that need a production deployment path
Medical imaging companies modernizing legacy PACS or RIS workflows
Digital health founders building clinician-facing tools requiring compliance from day one
Radiology groups seeking AI-assisted triage and prioritization tooling
The Transformation

Before & After

Before
✕ AI models existed only as Python notebooks with no path to clinical deployment
✕ Radiologists received images through a fragmented mix of legacy PACS and email-based hand-offs
✕ No structured audit trail for AI-generated findings, creating liability exposure
✕ Inference jobs ran ad hoc on a single GPU workstation with no queuing or monitoring
✕ Integration with hospital EMR and RIS systems was entirely manual and error-prone
After
✓ Radiologists access AI-annotated DICOM studies in a purpose-built browser workstation within seconds of scan completion
✓ Automated inference pipeline processes and prioritizes studies by urgency, routing critical findings to the top of the worklist
✓ Full audit trail on every AI prediction, reviewer action, and sign-off event for regulatory and liability purposes
✓ Scalable GPU-backed inference cluster handles peak hospital load with auto-scaling and job retry logic
✓ Bi-directional HL7 FHIR integration with major EMR systems eliminates manual data entry entirely
What We Built

Deliverables & Scope

Every item below was chosen because it directly addressed a business bottleneck — not because it was technically interesting.

01
Browser-based DICOM viewer with AI overlay rendering — windowing controls, multi-planar reconstruction, and finding annotation tools built on Cornerstone.js
02
Asynchronous inference pipeline using Celery and Redis to queue, execute, and deliver AI model results at scale across multiple GPU workers
03
HIPAA-compliant cloud infrastructure on AWS with encryption at rest and in transit, VPC isolation, and automated access logging
04
HL7 FHIR R4 integration layer connecting to Epic and Cerner for bidirectional patient, order, and result data exchange
05
Radiologist and clinician role-based portal with worklist management, structured reporting templates, and e-signature workflows
06
Real-time critical finding alerting system with configurable escalation rules and delivery via SMS, in-app notification, and pager integration
ROI Logic

Why This Generated
Real Business Value

Radiology departments are capacity-constrained — the bottleneck is radiologist time, not imaging volume. By surfacing AI pre-reads and prioritizing critical studies automatically, ImaginiHealth's platform allowed each radiologist to process significantly more studies per shift without sacrificing accuracy. For hospital systems paying per-read or managing radiologist burnout, this directly translates to revenue throughput and reduced outsourcing costs.

Key Outcomes
58%
Reduction in average radiology read time
3.2x
More scans processed per radiologist per shift
94%
AI finding accuracy rate validated in clinical trials
Why It Worked

The Decisions That
Made the Difference

Good execution matters. But the right early decisions matter more.

01
Techdots embedded a clinical workflow specialist alongside engineers from week one, ensuring the UI matched how radiologists actually work rather than how engineers assumed they did
02
The inference pipeline was decoupled from the viewer from day one, allowing AI models to be swapped, versioned, and A/B tested in production without redeployment of the frontend
03
HIPAA compliance and audit logging were treated as core architecture requirements, not post-launch additions — avoiding costly rework before hospital procurement sign-off
04
Structured reporting and HL7 FHIR integration were scoped in the first phase, which accelerated hospital IT approval cycles dramatically compared to custom one-off integrations
Tech Stack
Python (FastAPI) React.js PostgreSQL Redis AWS (ECS, S3, RDS, SQS)
Integrations
Epic via HL7 FHIR R4 API Cerner Millennium FHIR API Twilio (critical finding SMS alerts) Cornerstone.js DICOM rendering library NVIDIA Triton Inference Server
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