LAUSR

Abstract The Internet of Things (IoT) and artificial intelligence (AI) are two of the fastest-growing technologies in the world. With more people moving to cities, the concept of a smart city is not foreign. The idea of a smart city is based on transforming the healthcare sector by increasing its efficiency, lowering costs, and putting the focus back on a better patient care system. Implementing IoT and AI for remote healthcare monitoring (RHM) systems requires a deep understanding of different frameworks in smart cities. These frameworks occur in the form of underlying technologies, devices, systems, models, designs, use cases, and applications. The IoT-based RHM system mainly employs both AI and machine learning (ML) by gathering different records and datasets. On the other hand, ML methods are broadly used to create analytic representations and are incorporated into clinical decision support systems and diverse healthcare service forms. After carefully examining each factor in clinical decision support systems, a unique treatment, lifestyle advice, and care strategy are proposed to patients. The technology used helps to support healthcare applications and analyze activities, body temperature, heart rate, blood glucose, etcetera. Keeping this in mind, this paper provides a survey that focuses on the identification of the most relevant health Internet of things (H-IoT) applications supported by smart city infrastructure. This study also evaluates related technologies and systems for RHM services by understanding the most pertinent monitoring applications based on several models with different corresponding IoT-based sensors. Finally, this research contributes to scientific knowledge by highlighting the main limitations of the topic and recommending possible opportunities in this research area.