Pioneering
the Federated
Visual Age

Privacy-first federated AI that protects personal data while unlocking unprecedented AI capabilities. Train robust models without compromising user privacy.

Federated AI Network

Privacy-First Federated AI

Protecting personal data without compromising AI capabilities

Privacy-First Architecture

Federated learning that keeps sensitive biomarkers on the device while enabling model improvement across populations.

  • • Local data processing
  • • Privacy-first operation (no raw biomarker sharing)
  • • Real-time model refinement

Secure Aggregation

Aggregated insights are computed without exposing raw user data or identifiable biomarkers.

  • • Federated aggregation
  • • Compliance-aware design
  • • Scalable training

The Data Privacy Revolution in Healthcare AI

Healthcare information, images, voice and much more is locked on users' phones. Privacy and data security roles prevent access to third party apps.

No Centralized Data Storage

Sensitive data stays on the user's device, eliminating risks tied to cloud breaches.

Compliance by Design

Federated AI avoids raw data transmission required by HIPAA and GDPR.

De-Identification at Source

Biomarkers are processed locally, and never leave the phone.

Federated Network Architecture

User Device

Data remains secure on the user's device

Local AI Processing

Models train on-device without data exposure

Aggregated Insights

Only encrypted model updates are shared

Unlocking Data From Users' Devices

Access exabytes of data stored on users' devices including images and videos

Federated AI Devices

Smartwatches

Continuous health signals (heart, glucose, activity)

Smartphones

Secure patient interactions and medical records

AR/VR Headsets

Environmental, diagnostic, and behavioral data

Earbuds & Wearables

Speech, stress, and biofeedback tracking

Medical Devices

Clinical devices such as imaging scanners and point-of-care instruments

POC Diabetes Detection (Example)

Example using our federated AI technology — micro patterns visible to AI that are not obvious to the human eye

What do you see?

This proof-of-concept is an example demonstrating how our models detect micropatterns:

  • Microvascular patterns
  • Texture and pigmentation
  • Morphological changes

On Device De-Identification

Using a federated AI model — biomarkers are processed locally and are not transmitted off the device.

AI Vision vs Human Vision

Microvascular Analysis
Texture Recognition
Pattern Detection
Grad-CAM Analysis Example

Live Platform Dashboard

Real-time monitoring of federated learning models across the health app ecosystem

47,892
Active Training Devices
↑ 2,341 this week
12
Federated Models Running
↑ 3 new models
100%
Data Privacy Score
Zero data breaches
94.7%
Average Model Accuracy
↑ 1.2% improvement

Active Health Prediction Models

Facial Diabetes Screener

Computer Vision • Risk Assessment FDA Cleared

Active
Active Users
18,423
Accuracy
94.2%
Predictions Today
12,847
False Positive
3.1%
📸 Google Photos 🤳 Selfie Camera 📊 Health Records
Federated Training Stats
847
Rounds
98.4%
Participation
0
Data Leaks
Model Performance 94.2%

CGM Glucose Predictor

Time Series • Trend Analysis HIPAA

Active
Active Users
8,621
Accuracy
96.8%
Predictions/Hour
51,726
Avg Lead Time
45 min
📊 Dexcom G7 🩺 Libre 3 💊 Insulin Logs
Federated Training Stats
1,243
Rounds
99.1%
Participation
0
Data Leaks
Model Performance 96.8%

Multi-Modal Risk Assessor

Ensemble Model • Comprehensive Advanced

Training
Active Users
14,892
Accuracy
89.1%
Assessments Today
8,456
Risk Factors
24
📸 Facial Features 📊 CGM Data ❤️ Vitals
Federated Training Stats
412
Rounds
97.8%
Participation
0
Data Leaks
Training Progress 73%

Ready to Transform Healthcare?

Join us in pioneering the federated visual age. Contact us to learn more about our technology and partnerships.

Request Demo

For inquiries: info@visage-medical.com