Objectives: To examine the adoption of telerehabilitation services from the stakeholders’ perspective and to investigate recent advances and future challenges. Data Sources: A systematic review of English articles indexed by PubMed, Thomson Institute of Scientific Information’s Web of Science, and Elsevier’s Scopus between 1998 and 2020. Study Selection: The first author (N.N.) screened all titles and abstracts based on the eligibility criteria. Experimental and empirical articles such as randomized and nonrandomized controlled trials, pre-experimental studies, case studies, surveys, feasibility studies, qualitative descriptive studies, and cohort studies were all included in this review. Data Extraction: The first, second, and fourth authors (N.N., W.I., B.N.) independently extracted data using data fields predefined by the third author (M.B.). The data extracted through this review included study objective, study design, purpose of telerehabilitation, telerehabilitation equipment, patient/sample, age, disease, data collection methods, theory/framework, and adoption themes. Data Synthesis: A telerehabilitation adoption process model was proposed to highlight the significance of the readiness stage and to classify the primary studies. The articles were classified based on 6 adoption themes, namely users’ perception, perspective, and experience; users’ satisfaction; users’ acceptance and adherence; TeleRehab usability; individual readiness; and users’ motivation and awareness. Results: A total of 133 of 914 articles met the eligibility criteria. The majority of papers were randomized controlled trials (27%), followed by surveys (15%). Almost 49% of the papers examined the use of telerehabilitation technology in patients with nervous system problems, 23% examined physical disability disorders, 10% examined cardiovascular diseases, and 8% inspected pulmonary diseases. Conclusion: Research on the adoption of telerehabilitation is still in its infancy and needs further attention from researchers working in health care, especially in resource-limited countries. Indeed, studies on the adoption of telerehabilitation are essential to minimize implementation failure, as these studies will help to inform health care personnel and clients about successful adoption strategies.

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010–2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.