Blockchain-Based Secure Data Exchange for AI-Powered Diabetes Research and Personalized Therapy

Abstract

Diabetes remains one of the most data-intensive chronic diseases, demanding continuous monitoring, timely diagnostics, and personalized therapeutic decisions. However, the growing reliance on AI-based predictive systems in diabetes care is hindered by the lack of secure, interoperable data exchange frameworks across healthcare institutions. This paper proposes a blockchain-based secure data exchange model tailored for AI-powered diabetes research and personalized therapy. The system integrates blockchain’s immutability and decentralized trust mechanisms with federated AI architectures to ensure patient data confidentiality while enabling collaborative model training across multiple medical centers. The framework employs smart contracts for automated access control and consent management, ensuring compliance with data privacy standards such as HIPAA and GDPR. Through cryptographic hashing, distributed ledger synchronization, and on-chain auditing, the proposed model guarantees data provenance, transparency, and tamper-proof collaboration between hospitals, researchers, and AI systems. Preliminary evaluations indicate that this blockchain-integrated approach enhances data integrity and reduces privacy breaches while maintaining high model accuracy in personalized glucose regulation predictions. The study underscores the potential of blockchain as a backbone for secure, intelligent, and ethical healthcare data ecosystems in precision diabetes therapy.