# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 2 Related Work  
Various studies, which make use of several technologies, have been proposed in the medical sector. Many of them focus on the efficient detection of dangerous viruses and infectious diseases, along with optimized methods for finding and accelerating the procedure of drugs and antibiotics discovery. Due to the COVID-19 pandemic, in the last years, a remarkable effort on studying diseases behavior and ways to confine and eliminate them has been noticed. The following works represent studies related to healthcare innovation by implementing emerging technologies. They present frameworks and systems which are able to provide effective and efficient patient treatment. Four criteria were considered for selecting these works to be included in a comparative analysis: the works should be recently published; they propose systems or applications that contribute to the diagnosis, detection, or prevention of viruses or diseases and the treatment of patients or therapy discovery; they implement one or more of evolving technologies; and the results of these studies should be tested and validated. To the best of the authors' knowledge, the selected works provide an indicative sample that proves the benefits of inserting technology within the healthcare industry. Table 1 presents a concise comparative review of the related work. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Proposal**} & \multirow{2}{*}{**Proposal’s Scope**} & \multicolumn{2}{c}{**Diagnose/**} & \multirow{2}{*}{**Integrated**} & \multirow{2}{*}{**Analysis**} & **Energy** \\  & & & & **Treat/Detect/** & & & **Technologies** & **Analysis** & **Efficient** \\  & & & & & **Prevent** & & **System** & **Scenario** \\ \hline \multirow{8}{*}{[PERSON] et al. [17]} & \multirow{8}{*}{COVID-19 Intelligent Diagnosis and Treatment} & \multirow{8}{*}{**Prospasi’s Scope**} & \multirow{8}{*}{**Dignose COVID-19 earlier**} & \multirow{8}{*}{**Dignose COVID-19 earlier**} & \multirow{8}{*}{**Prospasi’s Scope**} & \multirow{8}{*}{**Dignose COVID-19 diagnosis and**} & \multirow{8}{*}{**Prospasi’s Scope**} & \multirow{8}{*}{**Dignose COVID-19 earlier**} \\  & &  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Paper** & **Proposal** & **Proposal’s Scope** & \begin{tabular}{c} **Diagnose/** \\ **Treat/Detect/** \\ **Prevent** \\ \end{tabular}  & \begin{tabular}{c} **Integrated** \\ **Technologies** \\ \end{tabular}  & \begin{tabular}{c} **Data** \\ **Analysis** \\ **System** \\ \end{tabular}  & \ \begin{tabular}{c} **Energy** \\ **Efficient** \\ **Scenario** \\ \end{tabular}  \\ \hline \hline \multirow{9}{*}{
\begin{tabular}{c} Framework to \\ detected COVID-19 \\ smartphone sensors \\  
The goal in [17] is the timely diagnosis and through the use of medical technology, called the \"COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)\", to achieve the optimal treatment. Cloud Computing (CC) technology, which is part of nCapp's functionality, contributes to real-time communication. According to this research, the diagnosis is automated when a suspicious sample is detected and then nCapp classifies the suspects into categories depending on the criticality of the situation. nCapp updates the system in real-time and makes the system a reputable tool for future illnesses as well. A detection framework for the COVID-19 virus which uses data gathered from the sensors that are integrated in smartphones such as cameras, microphones, and temperature and inactive sensors is proposed in [18]. Machine Learning (ML) practices are devoted for learning and obtaining knowledge about the disease symptoms based on the collected data. A quick and low-cost approach to virus detection is offered by this method. This is debatably conceivable because data acquired from the sensors of the smartphones have been exploited meritoriously in several discrete applications and the proposed method incorporates these applications together in a distinctive framework.