Healthcare AI Innovation

Medical AI Without the Trust Deadlock

Enable AI-powered diagnostics where patient data never leaves the hospital and AI models stay protected. Zero-knowledge proofs make the impossible possible.

Try the Demo How It Works

The Data Standoff

Medical AI innovation is stuck in a deadlock. Two parties who desperately need to collaborate simply cannot trust each other.

The Hospital

Data Holder

  • HIPAA violations: $50K-$1.5M per incident
  • GDPR fines: up to 4% of global revenue
  • Patient trust erosion
  • Cannot send data to external cloud APIs
Deadlock

The AI Vendor

Model Holder

  • Billions in R&D at risk of theft
  • Competitive advantage lost if copied
  • Cannot send model weights to hospitals
  • Reverse-engineering by malicious actors

The Result: Innovation stalls. Hospitals use outdated software. AI startups die because they can't access clinical data.

How Zkure Works

A "Trustless Sidecar" that acts as cryptographic notary—observing AI running on patient data and stamping a mathematical seal on the result.

1

Hospital Encrypts

Patient data enters the secure container on hospital premises. No data leaves.

HIPAA Compliant
2

AI Runs Locally

The AI model executes inside the container, generating diagnosis results.

Model Protected
3

ZK Proof Generated

A cryptographic proof attests the correct model was run on the data.

4KB Proof
4

Verify & Bill

Vendor verifies the proof in milliseconds and bills for the inference.

Instant Settlement

The Three Pillars

The Prover

Hospital Node

A local Docker container that holds patient data and runs the AI model. Data never leaves the hospital's firewall.

The Verifier

Vendor Cloud

A lightweight API that receives the Proof + Result and checks validity before issuing a bill or insurance claim.

The Circuit

The Math

The pre-compiled "map" of the AI model that ensures the computation was performed correctly. Built with EZKL + Halo2.

Technical Specifications

Built on proven cryptographic primitives optimized for inference verification.

Component Technology
ZK-Circuit Compiler EZKL (Easy Zero-Knowledge Learning)
Proof System Halo2 with KZG Commitments
Model Format ONNX (Open Neural Network Exchange)
Tabular Data Latency <2 seconds
Image Data Latency ~30 seconds (small images)
RAM Requirement ~16GB (Hospital Server)
Network Transmission ~4KB proof (vs 50MB image)

Business Model

Multiple revenue streams designed for the healthcare enterprise market.

Per-Diagnosis Fees

$0.50 - $5.00

Micro-fee per inference run. High-volume diagnostics (EKG, blood panels) at $0.50 x 10,000 daily patients = significant ARR.

Compliance-as-a-Service

$50K - $100K/year

EU AI Act conformity assessments. ZK proofs as automated audit logs for FDA/EMA regulators.

Enterprise License

Custom Pricing

On-premise deployment for large health systems. Federated learning marketplace for "clean data" rental.

See It In Action

Try our interactive demo to experience how ZK proofs enable privacy-preserving medical AI diagnostics.

Launch Interactive Demo