AI-Enabled Distributed Healthcare Framework for Secure and Resilient Remote Patient Monitoring

Cloud-based healthcare systems present an array of solutions to the needs of collecting patient data and dispensing well-processed reports to mention both patients’ and healthcare practitioners’ reports at any time and place. However, in times such systems face great challenge due to the fact that the system is open to a risk of a point failure, security loopholes, privacy issues, and lack of transparency. These challenges therefore bring an additional danger of the continuous and reliable provision of services. The proposed research will develop a novel healthcare framework that uses artificial intelligence to achieve decentralization of healthcare, the authenticated and managed IoT devices, trust, and transparency in the personal health record by means of AI-driven smart contracts in a public blockchain network. It is novel in the dynamic adaptive mechanism that analyzes and adjusts the operational environment behavior of the system. Real-time detection and minimization of dangers brought about by malicious IoT nodes with Adaptive Temporal Long-Short-Term-Memory (AT-LSTM) integration. In addition, the framework includes a module for predictive analytics in order to accurately predict system load and optimize resource allocation using artificial intelligence for stronger resilience in the healthcare system. The empirical studies suggest significant enhancements in critical performance measures such as data retrieval time, average delay, data transfer rate, and transaction charges. The lower energy usage, with averages of 11.77 mW for 3 devices. Such improvements suggest the capacity of the framework to convert secure and dependable remote patient monitoring and data management in healthcare.

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