### Related Work
The aforementioned studies emphasize the potential benefits of integrating different domains to develop more efficient and effective healthcare systems. This paper stands out by offering a comprehensive review of key literature concepts while highlighting the implementation of a robust IoMT architecture using practical services. Moreover, it effectively demonstrates how these technologies can be practically applied in smart and connected healthcare settings. This integration of theoretical and practical aspects of IoMT makes it a valuable addition to the field. The author's contributions in this context are highlighted below:  
* The exploration of smart and connected healthcare systems enabled by the IoMT has encompassed various dimensions, leveraging existing literature, bibliometric data, and global marketing analysis. In addition, an examination of the upcoming features of the Healthcare 5.0 paradigm has been conducted. * A comprehensive overview of the most promising enabling technologies for smart and connected healthcare systems has been provided, including IoMT, big data analytics, blockchain, healthcare cloud, fog, and edge computing. Additionally, the development requirements for these technologies have been elucidated. * The presentation of a robust and generalized healthcare architecture for the IoMT, taking into account security constraints for the devices, has been accomplished. Furthermore, promising practical applications/services that can benefit from this architecture have been highlighted. * Despite considerable investments in research and development, healthcare systems still face numerous challenges. These challenges have been discussed in relation to fundamental social needs, privacy, security vulnerabilities, regulations, and rights. Additionally, potential areas of research have been outlined to address these challenges and realize the vision of smart and connected healthcare. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **References** & **Methods Used** & **Purposes** \\ \hline \multirow{8}{*}{\begin{tabular}{c} Artificial \\ Intelligence/Machine \\ \end{tabular} } & [4] & ML and AI Algorithms & \begin{tabular}{c} Radio Imaging Technology, CT Scan, \\ X-ray, and Blood Sample Data \\ X-rays and CT scans \\ \end{tabular} \\  & [6] & L-RNN & \begin{tabular}{c} To predict the missing data in the \\ Hepatocellular Carcinoma data \\ \end{tabular} \\  & [7] & \begin{tabular}{c} ML algorithms: (Partial Tree (PART), \\ Random Forest, Naive Bayes, and \\ Random Tree) \\ \end{tabular} & \begin{tabular}{c} To detect brain tumor from the \\ MR images \\ \end{tabular} \\  & [8] & \begin{tabular}{c} Several AI/ML methods \\ Deep Reinforcement Learning and \\ Neural Networks methods \\ \end{tabular} & \
\begin{tabular}{c} Monitor Cardiovascular Diseases \\ Improvement in latency, error rate, etc. \\ \end{tabular} \\  & [10; 11] & Big Data Analytics &
\begin{tabular}{c} Overcoming issues such as green and \\ sustainable ICT \\ To detect DDoS and some \\ privacy attacks \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technologies based on existing literature in the healthcare domain. The remaining sections of this paper are organized as follows, as depicted in Figure 1. Section 2 provides an explanation of Smart and Connected Healthcare. Section 3 discusses the Bibliometric/Global Market Analysis, while Section 4 focuses on the evolution of healthcare. In Section 5, the Internet of Medical Things is explored, followed by Section 6, which delves into various enabling technologies in sustainable smart cities. Section 7 discusses the IoT healthcare services and applications. Section 8 highlights the open research challenges and research directions. Finally, Section 9 concludes the paper.