Innovative Applications of Large Language Models for Medical Record Access Audits

Abstract

This study explores the application of Mistral 8x7b, a state-of-the-art Large Language Model (LLM) with a mixture of experts architecture, in auditing access to medical records. By conducting a comprehensive evaluation, including both quantitative and qualitative analyses, we demonstrate that Mistral 8x7b significantly outperforms traditional audit methods in detecting unauthorized access, showcasing superior accuracy, precision, recall, and F1 score metrics. Additionally, our findings reveal notable improvements in computational efficiency, indicating the model's potential to enhance the privacy and security of healthcare data systems significantly. The research addresses the broader implications for healthcare privacy, AI ethics, and the responsible integration of AI technologies in sensitive domains. Despite acknowledging certain limitations and the necessity for further validation in real-world settings, this study underscores the transformative potential of advanced AI models like Mistral 8x7b in improving data security protocols and contributing to the safeguarding of patient information in an increasingly digital healthcare landscape.