Securing User Health Records: Leveraging Retrieval Augmented Generation (RAG) and Ethereum Integration for Enhanced Data Protection*

This study delves into the amalgamation of Retrieval Augmented Generation (RAG) with Large Language Models (LLMs) and Ethereum blockchain technology to bolster the security and confidentiality of user health records within healthcare systems. The research explores the transformative potential of this technological fusion in fortifying data protection, controlling access, and ensuring transparency in the management of sensitive health data. By harnessing RAG’s capacity for generating contextually pertinent responses, the linguistic prowess of LLMs, and the decentralized, tamper-resistant nature of Ethereum’s blockchain, the investigation seeks to address the pressing imperative of safeguarding user health records in the digital era. The paper examines the anticipated advantages, obstacles, and ramifications associated with the deployment of this innovative strategy, underscoring the paramount significance of data security and privacy in healthcare environments