Abstract COVID-19 and the associated epidemics have rapidly spread globally. Due to the contagiousness of the disease, the effort to stop the spread proves abortive. As a result, physical appointments with healthcare practitioners become a challenge. To address this challenge, Telemedicine (TM) is widely employed to provide healthcare services to infected individuals. Thus, this study aimed to provide a comprehensive literature review of TM applications based on machine learning (ML) for managing individuals infected with COVID-19 and related epidemics. The SLR methodology of this research considered published papers between 2015 and 2022. Out of 733 papers that were initially retrieved from six bibliographic databases, 24 primary studies were selected after passing the election criteria and quality assessment test. Analysis was carried out on the selected papers by answering six research questions related to the research topic. However, the findings from the SLR reveals that remote monitoring of patients during the pandemic outbreak is made easy through the application of TM and ML technique. The study also reveals different types of pandemic outbreaks, health data used by telemedicine programs, different types of ML, performance measures, and functionality of TM. Open research challenges and future research directions are also provided in this research domain. Therefore, this SLR can help researchers in future research and medical practitioners with ML-based TM applications in subsequence epidemic outbreaks.
Shuib et al. (Tue,) studied this question.