IoT-based telemedicine for disease prevention and health promotion: : State-of-the-Art

Numerous studies have focused on making telemedicine smart through the Internet of Things (IoT) technology. These works span a wide range of research areas to enhance telemedicine architecture such as network communications, artificial intelligence methods and techniques, IoT wearable sensors and hardware devices, smartphones and cloud computing. Accordingly, several telemedicine applications covering various human diseases have presented their works from a specific perspective and resulted in confusion regarding the IoT characteristics. Although such applications are useful and necessary for improving telemedicine contexts related to monitoring, detection and diagnostics, deriving an overall picture of how IoT characteristics are currently integrated with the telemedicine architecture is difficult. Accordingly, this study complements the academic literature with a systematic review covering all main aspects of advances in IoT-based telemedicine architecture. This study also provides a state-of-the-art telemedicine classification taxonomy under IoT and reviews works in different fields in relation to that classification. To this end, this study checked the ScienceDirect, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Web of Science databases. A total of 2121 papers were collected from 2014 to July 2020. The retrieved articles were filtered according to the defined inclusion criteria. A final set of 141 articles were selected and classified into two categories, each followed by subcategories and sections. The first category includes an IoT-based telemedicine network that accounts for 24.11% (n = 34/141). The second category includes IoT-based telemedicine healthcare services and applications that account for 75.89% (n = 107/141). This multi-field systematic review has exposed new research opportunities, motivations, recommendations and challenges that need attention for the synergistic integration of interdisciplinary works. This extensive study also lists a set of open issues and provides innovative key solutions along with a systematic review. The classification of diseases under IoT-based telemedicine is divided into 14 groups. Furthermore, the crossover in our taxonomy is demonstrated. The lifecycle of the context of IoT-based telemedicine healthcare applications is mapped for the first time, including the procedure sequencing and definition for each context. We believe that this study is a useful guide for researchers and practitioners in providing direction and valuable information for future research. This study can also address the ambiguity in the trends in IoT-based telemedicine.