Artificial Intelligence, Iot and Smart Sensing Technologies for Advanced Healthcare Monitoring, Disease Prediction and Clinical Informatics

Abstract

Advanced healthcare monitoring, predictive disease diagnosis and healthcare informatics are emerging opportunities thanks to the rapid advancements of Artificial Intelligence, IoT and smart sensing technologies. This research suggests a Hybrid Edge-AI methodology based on CNN–BiLSTM with Attention and Explainable AI that was developed using TensorFlow to assist with real-time and accurate healthcare decision making. Smart sensors that can be equipped with IoT technology gather and transmit physiological data about individuals (ECG, heart rate, blood pressure, oxygen saturation, and body temperature), continuously. CNN model learns important spatial health features, and BiLSTM layer learns temporal variations in continuous patient data. The attention mechanism enhances the predictive power by highlighting clinically relevant signals and Explainable AI gives explainable reasons for disease-risk classification. Edge computing helps to quickly respond to requests and decrease reliance on cloud-based processing. The proposed framework aims to improve remote patient monitoring, timely detection of disease, generation of alerts during emergencies, and providing a clinical decision support system, with a scalable and comprehensible solution for the current digital healthcare systems.