How Medow Works

Zero raw data movement. Continuous improvement. Privacy by design.

Federated Learning Workflow

Zero raw data movement — only encrypted model updates travel.

Step 1

Local Data Ingested

Hospitals ingest imaging, patient records, and clinical data via PACS/EHR using HL7/FHIR standards. All data stays on-premise.

Step 2

Edge Model Training

On-site devices (NVIDIA Jetson or hospital servers) train AI models locally on private hospital data. No data leaves the building.

Step 3

Encrypted Update Transfer

Only encrypted model weight updates (not data) are sent to the central Snowflake Engine via TLS 1.3. Raw data never moves.

Step 4

Model Aggregation & Redeploy

The Snowflake Engine aggregates updates from all hospitals using FedAvg, producing an improved global model that is deployed back to each site.

Cycle repeats — models improve with every iteration

Closed-Loop Feedback

Doctor-in-the-Loop Labeling

Clinicians verify AI predictions using Grad-CAM heatmap explanations and correct edge cases. Their feedback creates proprietary, high-fidelity datasets that accelerate accuracy gains and maintain rapid data velocity over rivals.

Heatmap overlays show AI reasoning
Doctors approve, reject, or refine findings
Each interaction improves the model
Creates defensible proprietary datasets

Every doctor interaction acts as a micro-labeling event, building the highest-quality domain-tuned healthcare model over time.

The Network Flywheel

A competitive moat that compounds with every new partner.

More Hospitals

More Diverse Data

Better Models

More Hospitals

Network effects that rivals cannot replicate — each hospital makes the entire network smarter.

See it in action

Explore the platform demos or request a pilot for your hospital.

Explore the Platform Request a Pilot