# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### Telemedicine
By using a Raspberry Pi and a commercially available webcam, the wearable solution of medical tele-tutoring REC-VISIO 118 can provide a stabilized video of the situation, mainly for first aid interventions, that can be accessed through a web interface in real-time through a 4G connection, leading to a faster pre-diagnosis for time-dependent conditions such as heart attacks or strokes [46]. The need for artificial intelligence is emphasized in [47], pointing to the ever-expanding volume of sensor data that must be interpreted in order to find any useful information, while satisfying the common needs of the stakeholder groups responsible for the actual development and implementation. Considering that the second cause of death in patients that overcome cancer is cardiovascular disease, the COVID-19 pandemic has raised concerns regarding cardiovascular toxicities and the way clinicians react to them, taking into account innovations during and after the pandemic, as follows [48]:  
1. Digital health technologies such as mobile health (mHealth) and wearables help patients to monitor their own health and reduce the number of unneeded hospital visits, detect abnormal heart rates (Apple Watch), and collect and analyze health data, caring for patients while keeping them safe during the pandemic. 2. In the field of telemedicine, many freely accessible cloud-based solutions have become compliant with laws that protect patient information, making cardio-oncology able to benefit from telehealth. 3. Social media-assisted telehealth, through its various means of information propagation, has an important impact on prevention and innovation, especially in cardio-oncology. 4. Artificial intelligence is without a doubt one of the foundation elements of many eHealth implementations, be it mHealth, wearables, remote patient monitoring or other applications. By connecting artificial intelligence algorithms with social media platforms, these systems can not only predict some conditions, but even help locate or get in contact with the patient. Table 4 brings together relevant telemedicine applications, along with a description of their results and the technologies they used to implement them. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Work Description** & **Results** & **Technologies Employed** \\ \hline \multirow{3}{*}{[44]} & Telemedicine solution for & Shorter patient waiting time for first consultation, & Artificial intelligence, video \\  & eyecare in remote Western & reduction in costs, availability in remote regions, & conferencing, store and forward \\ \cline{1-1}  & Australia & detecting multiple conditions remotely; faced & methods \\ \hline \multirow{3}{*}{[45]} & A review about the evolving & \multirow{3}{*}{Describes the use of teleophthalmology, expanding [44].} & Artificial intelligence, video \\ \cline{1-1}  & role of teleophthalmology in a & & expanding [44]. & conferencing \\ \cline{1-1}  & COVID-19 pandemic & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Telemedicine applications.