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Medical Intelligence Tokenization Model

MedNexus uses a specialized tokenization model designed specifically for medical intelligence assets, leveraging the ERC-7857 INFT standard to create transferable medical expertise.

Core Components​

1. Medical Intelligence INFTs (ERC-7857)​

Tokenized medical expertise with secure, transferable intelligence:

  • Diagnostic Protocol INFTs: AI models trained on specific medical conditions with encrypted diagnostic algorithms
  • Treatment Algorithm INFTs: Evidence-based decision trees with outcome tracking and success metrics
  • Research Methodology INFTs: Proven clinical trial protocols and research frameworks
  • Specialist Consultation INFTs: Access to global medical expertise and consultation services

ERC-7857 Features for Medical Applications:

  • Privacy-Preserving Metadata: Encrypts sensitive medical intelligence while maintaining transferability
  • Secure Knowledge Transfer: Both ownership AND encrypted medical knowledge transfer together
  • Dynamic Medical Data: Supports evolving AI capabilities as medical knowledge advances
  • Authorized Medical Usage: Grant consultation rights without ownership transfer

2. Medical Data Manifests (0G Storage)​

Encrypted medical datasets with granular access controls:

  • Patient Case Manifests: Anonymized case studies with symptom patterns and treatment outcomes
  • Research Dataset Manifests: Clinical trial data, longitudinal studies, and medical research collections
  • Medical Imagery Manifests: Diagnostic images, scans, and visual medical data
  • Institutional Knowledge Manifests: Hospital protocols, treatment guidelines, and medical procedures

Security Features:

  • Client-side encryption before 0G Storage upload
  • Institutional access control with patient consent verification
  • Medical metadata schemas for HIPAA/GDPR compliance
  • Verifiable provenance tracking for regulatory audits

3. Medical Execution Receipts (0G DA)​

Immutable verification of medical intelligence usage:

  • Consultation Receipts: Verified records of specialist consultations and recommendations
  • AI Analysis Receipts: Proof of medical AI processing and diagnostic assistance
  • Research Collaboration Receipts: Multi-institutional study coordination and results verification
  • Treatment Outcome Receipts: Longitudinal tracking of patient responses and medical effectiveness

Medical Tokenization Flow​

Medical Intelligence Creation​

  1. Medical Expert develops diagnostic algorithm or treatment protocol
  2. Knowledge Validation through peer review and clinical testing
  3. INFT Minting using ERC-7857 with encrypted medical intelligence
  4. Revenue Sharing Setup for collaborative medical contributions

Medical Consultation Usage​

  1. Doctor discovers relevant Medical Intelligence INFT for patient case
  2. Access Authorization through institutional verification and patient consent
  3. Secure Execution of medical intelligence on encrypted patient data
  4. Verified Results with immutable receipt on 0G DA
  5. Automatic Payment distribution to INFT owner and data contributors

Research Collaboration​

  1. Research Institution shares anonymized medical datasets
  2. Global Matching connects researchers with relevant patient populations
  3. Multi-Institutional Coordination through smart contract automation
  4. Collaborative Results with verified provenance and contribution tracking

Revenue Distribution Model​

Medical Intelligence Licensing​

  • 70% to Medical Intelligence INFT creator (doctor/researcher who developed the expertise)
  • 20% to Medical Data contributor (institution/patient providing case data)
  • 10% to MedNexus platform for infrastructure and compliance management

Research Collaboration Royalties​

  • 40% to Primary Research Institution leading the study
  • 40% distributed among Contributing Institutions based on patient/data contribution
  • 20% to Supporting Medical Intelligence INFT providers

Compliance & Security​

Medical Privacy Protection​

  • End-to-End Encryption: All medical data encrypted before leaving institutional control
  • Zero-Knowledge Processing: Medical AI analysis without exposing raw patient data
  • Granular Consent: Patient-controlled permissions for every medical data use case
  • Audit Trail Transparency: Complete immutable record of all medical data access

Regulatory Compliance​

  • HIPAA Compliance: Built-in privacy safeguards for US healthcare regulations
  • GDPR Compliance: European data protection standards with right to erasure
  • International Standards: Adaptable framework for global medical regulations
  • Institutional Verification: Comprehensive credentialing for medical professionals and institutions

This tokenization model ensures that medical expertise can be shared globally while maintaining complete privacy, regulatory compliance, and fair compensation for all contributors to the medical knowledge ecosystem.