Revolutionizing Healthcare with AI and Deep Learning: Smart Health Monitoring for Early Detection and Enhanced Patient Care

Abstract

  The evolution of technology has reshaped the healthcare industry, offering innovative solutions to challenges in disease prevention, monitoring, and control. With the advent of Industry 5.0 and 5G, cost-effective sensors now enable real-time health monitoring, significantly enhancing patient care. This study investigates the application of Artificial Intelligence (AI) and Deep Learning (DL) in Smart Health Monitoring (SHM) systems, emphasizing early detection of chronic conditions and proactive healthcare. Key techniques, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid RNN-LSTM-CNN framework, were applied to analyze health data, achieving a 92.3% accuracy rate in disease detection with an AUC of 0.95. The hybrid model demonstrated superior performance in reducing readmission rates and diagnostic errors while improving median survival times for AI-enhanced patients to 48 months compared to 42 months for standard care. A cost-benefit analysis highlighted its economic viability, with an incremental cost-effectiveness ratio (ICER) of $100 per readmission reduced and a net benefit of $100,000. The integration of blockchain ensured secure handling of sensitive patient information, while cloud computing enhanced scalability and real-time functionality. Despite these advancements, challenges such as data diversity, system compatibility, and high implementation costs remain. This review underscores the transformative potential of AI-driven SHM systems to create a predictive, patient-centered, and economically sustainable healthcare ecosystem, while addressing the barriers to their broader adoption and ensuring ethical AI governance.