A Systematic Review of Artificial Intelligence-Based Health Monitoring Systems for Improved Diagnosis and Patient Care
Keywords:
Artificial Intelligence, Health Monitoring, Predictive Analytics, Wearable Devices, Personalized HealthcareAbstract
The integration of Artificial Intelligence (AI) into health monitoring systems has revolutionized the way medical data is collected, processed, and analyzed for timely interventions. Traditional health monitoring devices often focus on data acquisition without advanced predictive capabilities, whereas AI-driven systems can interpret complex datasets, detect anomalies, and provide personalized insights for preventive healthcare. These systems employ machine learning algorithms, deep learning models, and natural language processing to identify patterns in physiological parameters such as heart rate, blood pressure, glucose levels, and respiratory functions. By leveraging wearable devices, Internet of Things (IoT) technologies, and cloud-based platforms, AI-powered health monitoring ensures continuous tracking and real-time diagnosis, reducing the risk of delayed treatment. Furthermore, predictive analytics enables early detection of chronic diseases, improves patient outcomes, and reduces healthcare costs. Despite their potential, challenges such as data privacy, algorithmic transparency, and integration into existing healthcare infrastructure remain critical issues to address. This review explores current advancements, applications, and limitations of AI-enabled health monitoring systems while emphasizing their role in shaping the future of personalized healthcare and remote patient management.
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