Enhanced Healthcare AI Assistance Through Blockchain-Integrated Data Access

Abstract

Generative Artificial Intelligence (AI) powered by Large Language Models (LLMs) has emerged as a valuable tool in healthcare assistance, offering a user-friendly interface with narrative responses. However, improving response quality for healthcare AI assistance, it is challenging to obtain necessitates access to sensitive patient data, such as medical histories. To enhance the privacy and security of medical data delivery, this paper proposes a healthcare AI system that integrates LLM-based AI with blockchain technology. Specifically, a consortium blockchain architecture is adapted for secure handling of sensitive medical data in healthcare AI applications. Additionally, the system leverages edge-computing AI to enhance reliability in healthcare services. Simulation results demonstrate that the proposed consortium blockchain integration improves scalability and efficiency compared to public blockchain solutions. Furthermore, the system achieves an improved semantic similarity metric of 0.827 on the MedQuAD dataset, validating the DeepSeek-R1 LLM’s effectiveness within an edge-computing healthcare AI framework. The proposed system design provides a reliable framework for future healthcare AI assistants, ensuring enhanced response quality, data privacy and security, and system reliability.

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