Enhancing Privacy Transparency in Remote Patient Monitoring with Explainable AI

Abstract

This paper presents an Explainable Privacy-Preserving Intelligent System for Monitoring (X-PRISM) framework designed to enhance transparency and privacy in AI-based remote patient monitoring (RPM) systems. The framework addresses the critical demand for explainable AI (XAI) in healthcare by integrating explainability techniques, such as SHapley Additive exPlanations (SHAP), to provide clarifications and reasoning behind AI-driven decisions based on key patient metrics such as heart rate and body temperature. X-PRISM implements pseudonymization and encryption to improve privacy and secure sensitive healthcare data while ensuring compliance with global regulations such as the General Data Protection Regulation (GDPR). Federated learning plays a vital role in the framework by enabling decentralized training of AI models across multiple healthcare nodes without directly sharing patient data. The layered architecture of X-PRISM enables seamless data collection from wearables and IoT devices, preprocessing in Azure Cloud, and AI model development using TensorFlow. X-PRISM is designed to offer transparent decision-making, protect patient data through decentralized training, and enable real-time interpretable feedback in RPM applications. Although the framework is not empirically tested in this study, it is presented as a foundation for future research and development in ethical AI deployment in healthcare. Existing gaps in RPM systems and synthesizing best practices in AI explainability and data privacy are reviewed in this study, which lays the foundation for developing secure, interpretable, and regulatory-compliant RPM systems. X-PRISM establishes the groundwork for advanced ethical, scalable, and trustworthy AI applications in RPM by addressing the dual goals of privacy and explainability, thereby enhancing patient trust and healthcare outcomes.