Abstract
Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.
Highlights
Interactive devices can refer to any stationary hardware or a mobile component that facilitates the interaction between the environment of the user and the human user like wearable devices, speech recognition devices, and smartphones [14]. e ambient devices can refer to any kind of consumer electronics that is categorised by their capability of perceiving at a glance like smart applications, smoke sensors, and motion sensors
It has been recommended that different models have been proposed in several studies as discussed in this study; these models must be validated further and implemented in different domains to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively [47]. e main issues identified in the healthcare systems is the late diagnosis, wrong treatment, and misinterpretation due to which it is required that the proper systems must be installed with the incorporation of advanced software, tools, and technologies [25]
Edge-Artificial Intelligence (AI) and IoMT are the advanced technologies that are being currently used in various parts of connected healthcare in Smart Cities. e use of these technologies in the monitoring and management of connected healthcare can prove to be very beneficial as they can reduce the required human efforts and improve the efficiency of management
Summary
To present a cloud-based smart healthcare monitoring model to effectively interact with the environment, different nearby smart devices, and stakeholders of smart cities for accessible and affordable healthcare To propose a hybrid deep learning model to overcome the issue related to the filtration of duplicated questions in healthcare. Proposed an SEG 3.0 as a methodology. To encourage real-time analysis and to present the concept of “mobile edge computing”. To propose a conceptual framework known as “big data–enabled smart healthcare system framework”. To use 2 different transfer learning methods for retraining the VGG-Net and gained 2 different networks which include VGG-mi-1 and VGG-mi-2. To conduct a review on smart and connected health (SCH). To explore the implication of the latest trends in connected healthcare including IoT and. To explore the needs of the extraction of big data urban population
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