[Retracted] New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Table 1

Summary of Findings of the Selected Studies based on Edge-AI.

No	References	Study design	Objectives	Findings	Limitations
1	Amin and Hossain (2021) [18]	Qualitative analysis	To evaluate the recent and revolutionising frameworks of edge computing, effective technologies for smart healthcare services, the challenges and opportunities of different scenarios related to applications. To comprehensively analyse the usage of classification based on cutting edge AI and techniques that can be implemented for edge intelligence	The contribution of this study is that it has provided potential recommendations based on research for enhancing the services related to Edge AI computation for healthcare services in smart cities. Along with it, IoT solutions are also highlighted focusing on the edge platform for the growth of the healthcare industry	A limited number of researchers are available
2	Syed et al. (2021) [15]	Qualitative analysis	To provide holistic coverage of the Internet of Things in Smart Cities. To review the most prevalent applications and practices in different domains of Smart City. To examine the challenges of adopting IoT systems for smart cities along with mitigation measures	Broad coverage of IoT in Smart Cities is presented as an important enabling of smart city services. The privacy and security issues faced by IoT also have been discussed in detail	Different research methods could have been used for making DL (deep learning) and ML (machine learning) more explainable further
3	Umair et al. (2021) [16]	Qualitative analysis	Impact of covid-19 on the adoption of IoT	Identified the challenges that are needed to be addressed	Further research is required
4	Amin and Hossain (2021) [18]	Qualitative analysis— survey	To evaluate the recent and revolutionising frameworks of edge computing, effective technologies for smart healthcare services, the challenges and opportunities of different scenarios related to applications. To comprehensively analyse the usage of classification based on cutting edge AI and techniques that can be implemented for edge intelligence	The contribution of this study is that it has provided potential recommendations based on research for enhancing the services related to Edge AI computation for healthcare services in smart cities. Along with it, IoT solutions are also highlighted focusing on the edge platform for the growth of the healthcare industry	A limited number of researchers are available
5	Hossain et al. (2020) [21]	Quantitative study—experiment design	To propose a B5G model that is based on utilising the 5G network’s functionality related to high bandwidth, low latency, for detecting the cases of COVID-19	The framework was found to efficiently monitor the activities related to mask-wearing, body temperature, and social distancing	Only 3 DL models are used and the framework is required to be tested in future with the protease sequence analysis
6	Nawaz et al. (2019) [22]	Quantitative study—cross-sectional study design	To propose an Ethereum Blockchain-based framework with edge AI	It helped to overcome the challenge of increased security issues due to the addition of new coatings in the network design	Cause-and-effect relationship is not presented due to the nature of the study design
7	S. Tuli et al. (2020) [23]	Qualitative study design	To present a vision of implementing a holistic framework that can meet the increasing needs of healthcare and patients	The model helped to identify challenges, opportunities, and current trends in the healthcare industry,.	Used limited deep learning techniques to predict failures
8	F. Alshehri and G. Muhammad (2020) [24]	Qualitative study design— a comprehensive survey	To evaluate the studies based on IoT, medical signals, IoMT, AI-edge, and cloud services	The major challenges of smart health care are identified, which includes device communication, the barrier to information management, security issues, sensors’ interoperability, device management, and use of AI efficiency. It has also been identified that IoMT devices can help to diagnose disease and to reduce illness	Researches conducted after 2020 are not included
9	A. Imran et al. (2020) [26]	Qualitative study design— secondary literature review	To evaluate the studies based on edge computing and fog computing	Increasing challenges can be minimised by the implication systems based on data processing on the network nodes and layers which is known as edge computing and fog computing, respectively	Researches conducted after 2020 are not included
10	L. Greco et al. (2020) [27]	Qualitative study design— secondary literature review	To evaluate the studies based on providing solutions to the smart healthcare services through edge computing and fog computing	Presented solutions from the initial stage of health monitoring feasibility through from wearable sensors till the detailed discussion related to the modern trends in edge and for computing for connected healthcare	Researches conducted after 2020 are not included
11	Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui (2020) [28]	Review-based qualitative study	To conduct a review on Edge intelligence	Edge caching, edge inference, edge training, and edge offloading are identified as four key components	Researchers also discussed edge intelligence from various perspectives, such as applicable scenarios, performance, methodology, and so on, and summarised their benefits and drawbacks.