Blockchain-Enabled Secure Data Sharing for AI-Driven Diabetes Research and Personalized Treatment

Abstract

The integration of blockchain technology into healthcare research offers transformative potential for secure, transparent, and decentralized data management. In the field of diabetes research, where vast volumes of heterogeneous patient data are continuously generated from clinical records, wearable sensors, and laboratory studies, data sharing remains hindered by privacy, interoperability, and ownership challenges. This study proposes a blockchain-enabled framework to facilitate secure and auditable data sharing for artificial intelligence (AI)-driven diabetes research and personalized treatment. The model ensures data integrity through distributed ledger technology while allowing encrypted, permissioned access for AI models to analyse patient datasets. Smart contracts automate consent management, while federated learning enables AI systems to train on decentralized data without exposing sensitive information. The proposed approach enhances transparency, improves diagnostic accuracy, and accelerates the development of predictive algorithms tailored to individual metabolic profiles. Through simulation-based validation, the framework demonstrates low latency in data retrieval, high throughput, and robust resistance to unauthorized access. This study establishes a scalable model for next-generation medical data ecosystems that align with ethical, technical, and regulatory requirements of modern digital health infrastructures