Leveraging Generative AI and Large Language Models for Secure and Efficient Healthcare Data Management

Abstract

The rapid adoption of Generative AI (GenAI) and Large Language Models (LLMs) in healthcare systems introduces transformative opportunities for managing and securing electronic health records (EHRs). This study investigates the integration of LLMs, such as GPT and BERT, with blockchain technology to enhance the security, accessibility, and processing efficiency of sensitive medical data. The system employs Solidity-based smart contracts on the Ethereum blockchain to enable decentralized, transparent, and secure transactions of patient data, ensuring compliance with privacy regulations while reducing administrative burdens. Key methods include training transformer-based LLMs to query and retrieve EHRs with precision and confidentiality, achieving over 95% accuracy in extracting critical patient information. Blockchain technology is leveraged to create immutable data records, while MetaMask provides encrypted, secure access to authorized stakeholders. Differential privacy and federated learning further enhance data protection by enabling secure collaboration without compromising patient confidentiality. The real-time deployment of adaptive feedback loops improves system reliability by addressing biases and maintaining high precision and recall. The results demonstrate a 50% improvement in data processing speed and a 35% reduction in security breaches compared to conventional healthcare data management systems. This hybrid approach highlights the synergy between GenAI and blockchain, providing a scalable and secure framework for EHR management. By combining advanced AI capabilities with decentralized security mechanisms, this study sets the foundation for innovative applications in healthcare, enabling efficient, trustworthy, and patient-centric data management solutions.