https://doi.org/10.1016/j.parepi.2022.e00266

Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh

Recent years have witnessed increased pressure across the global healthcare system during the COVID-19 pandemic. The COVID-19 pandemic shattered existing healthcare operations and taught us the importance of a resilient and sustainable healthcare system. Digitisation, specifically adoption of Artificial Intelligence (AI) has positively contributed to developing a resilient healthcare system in recent past. To understand how AI contributes to building a resilient and sustainable healthcare system, this study based on systematic literature review of 89 articles extracted from Scopus and Web of Science databases is conducted. The study is organised around several key themes such as applications, benefits, and challenges of using AI technology in healthcare sector. It is observed that AI has wide applications in radiology, surgery, medical, research, and development of healthcare sector. Based on the analysis, a research framework is proposed using an extended Antecedents, Practices, and Outcomes (APO) framework. This framework comprises AI applications’ antecedents, practices, and outcomes for building a resilient and sustainable healthcare system. Consequently, three propositions are drawn in this study. Furthermore, our study has adopted the theory, context and methodology (TCM) framework to provide future research directions, which can be used as a reference point for future studies. 

Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions

This study examines factors influencing patients' adoption of the IoT for e-Health Management System (e-HMS). A conceptual framework is built by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model to achieve the research objectives. The constructs viz. performance expectations, perceived healthcare threat, facilitation, and trust were employed to analyze the behaviour intention of healthcare IoT devices in the medical context. A questionnaire was prepared and circulated to 210 IoT healthcare users. Further, a synergy of Smart-PLS and ANN was used to determine factors influencing adopting the IoT for e-HMS. The study yields a novel insight that would render vital benefits to users and service providers. The outcomes of this research can assist IoT inventors, medicinal specialists, and vendors to enhance the optimization of e-HMS. The results make some new reporting facts to improve IoT 4.0 practices in the e-healthcare system and structural model assessment.

Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach

Corona virus disease 2019 (COVID-19) is an enduring extensive pandemic disease globally that infected several million people, occasionally leads to fatal. COVID-19 transfers from one person to another, the prevention is possible only through maintaining social distance. This paper proposed Internet of Things (IoT)-based smart wearable tags for social distance monitoring to prevent COVID-19. The advantage of the proposed system is that it alarms the person with the proposed tag and also entries the data in the IoT ledger of the channel for further analysis. The proposed system responds adequately so that social distancing can be maintained and pandemics can be prevented from further spreading. The analysis has been carried out using an experimental setup, and Thingspeak is used as an IoT channel for data analytics. The result shows the successfulness of the developed system in the prevention of pandemics with social distancing monitoring. 

Design of Social Distance Monitoring Approach Using Wearable Smart Tags in 5G IoT Environment During Pandemic Conditions

Corona virus, a novel virus, was firstly reported in Wuhan, China on December 2019. From then, it has been started spreading to the entire world. The virus can spread easily from the infected person to normal person. The infected person’s human body develops high fever which is the main symptoms of this novel symptoms of Corona virus. To prevent the spread, infrared thermometer for thermal screening has been utilized. To find the infectee among the crowd, each person body temperature has to be manually checked. This process is time-consuming and has a threat of getting spread for the person who does the screening process. Hence, to overcome the problems, we propose a novel way of screening using smart spectacle with least human interactions. With the help of IoT technology and thermal camera technology, the screening process will become more safety and faster compared to the conventional screening. Additionally, the facial recognition method is exploited to get pedestrian information. The proposed smart spectacle system has a greater potential to identify the Corona virus and reduce the spreading. 

Design of Smart Spectacle in 5G-IoT Environment to Detect and Prevent Corona Virus Variants

The pandemic situation has pretentious the habitual life of the human, it also has surpassed the regional, social, business activities and forced human society to live in a limited boundary. In this paper, the application of the internet of things (IoT) and machine learning (ML) based system to combat pandemic situation in health care application has been discussed. The developed ML and IoT based monitoring system help in tracking the infected persons from the previous data and makes them get isolate from the non-infected person. The developed ML combined IoT system uses parallel computing in tracking the pandemic disease and also in the prevention of pandemic disease by predicting and analysing the data using artificial intelligence. The implementation of ML-based IoT in the pandemic situation in healthcare application has proved its performance in tracking and prevents the spreading of pandemic disease. It also further has a positive impact on reducing medical costs and has recorded improved treatment for infected patients. The proposed methodology has an accuracy of 93 % in monitoring and tracking. The result obtained help in preventing the spread of the pandemic and provide support to the healthcare system.

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M. Rajesh

Papers

Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation.