Improving Quality of Life with Emerging AI and IoT Based Healthcare Monitoring Systems

Two of the technologies that are spreading at the fastest rates in the whole world are artificial intelligence (AI) and the Internet of Things (IoT). Considering the growing number of people who are relocating to urban areas, the idea of a smart city is not new. A smart city is a concept that is centered on the notion of improving the healthcare sector by enhancing its efficiency, cutting expenses, and placing the emphasis back on a better patient care system. In order to successfully use Internet of Things and artificial intelligence for remote healthcare monitoring systems, one must have a comprehensive grasp of the many frameworks that are utilized in smart cities. Traditional healthcare services are not only prone to causing patients to pass away, but they also have the potential to cause delays, squander resources, and result in financial loss. When utilized in combination with the intelligence and prediction capabilities of the Internet of Things (IoT), frequent Remote Patient Monitoring (RPM) at home, at work, or at a hospital may assist persons who especially need it in overcoming challenges that are given by conventional healthcare facilities. The Internet of Things (IoT)-based RPM may act as a precursory warning system for approaching circumstances that, if ignored or treatment is delayed, might result in major health concerns or even patient death. Wearable technologies, sensor networks, and other digital infrastructure are employed in this system. Wearable gadgets (biosensors) that integrate with the internet of things allow medical professionals to obtain real-time vital indicators from their patients. Through the development of a framework that is supported by both the Internet of Things (IoT) and Artificial Intelligence (AI), the purpose of this article is to facilitate the implementation of a Remote Patient Monitoring System that is equipped with data analytics capabilities. Following the implementation of a Remote Patient Monitoring System for the purpose of data collecting, we suggested an algorithm for the diagnosis of diseases.