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With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. This refers to systems in which all the healthcare stakeholders are connected to each other. These systems employ novel technologies, such as IoT, edge computing, and artificial intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, such systems encounter many restrictions and require new policies. By moving the computation and processing closer to the data sources and end-users, fog becomes edge computing which can reduce latency, bandwidth usage, and energy consumption. To the best of our knowledge, there is no systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to examine the state-of-the-art research in this area. We have reviewed a significant number of papers in this area and divided them into two main taxonomies, patient-centric and process-centric techniques. Furthermore, essential factors, such as available data sets and parameters like accuracy, mobility, and data rates are described and examined. Our aim is to bridge the gap between edge computing and connected healthcare solutions by discussing the challenges and highlighting future trends.
Hayyolalam et al. (Tue,) studied this question.