# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 3 Technological Challenges  
The challenge with health-related systems is that they must have increased accuracy and sensitivity so that a patient is not misdiagnosed. Therefore, it is necessary to provide a sufficient knowledge to the medical staff in order to give them the ability to validate the correctness of this decision. Three phases constitute the superior aim of the medical sector: immediate detection, accurate diagnosis, and effective treatment of a patient. Although the healthcare industry has done some important steps to improve itself, still conventional medical methods show limited potentials in these phases. Many challenges need to be addressed for humanity in order to have a completed healthcare system.  
Nowadays, it is fact that the integration of cutting-edge technologies can successfully support the conventional detection, diagnosis, and treatment methods in order to deal with different microbiological diseases, infectious viruses, common illnesses, and general disorders. A smart healthcare system can provide accuracy and speed in detection, diagnosis, and treatment of several issues from which the human population suffers and deregulate their daily life.  
Several recent studies have shown that the speedy development of both mobile and wired networks has generated rapid growth of data. The management, analysis, and transfer of such Big Data (BD) generate new challenges which are related to their life cycle, representation, quality, storage, confidentiality, total effectiveness, etc. [26]. BD is transferred through several of these levels in a way that leaves gaps relevant to its utilization. For this reason, optimized algorithms and methods are necessary to solve such problems [27]. However, many countries do not have the proper infrastructure to host and apply such emerging technologies.  
In addition, the challenges posed by the traditional methods of volumetric data display incorporate the information overburden, resulting from the generated volume of data sets. Another challenge is the detection of small faults, where a deep analysis with slice analysis will entail a long time. There is also the task of mentally creating a 3D image by looking at the slices. Therefore, volume visualization is often used in medical applications such as computed tomography and magnetic resonance imaging. Subsequent images of volume visualization are translucent and blurred, making intensity difficult to perceive [28].  
Additionally, while surgeons rely heavily on the sense of touch in the operating room, surgical design systems in general do not exploit the sense of touch to supplement the visual interface. Thus, scheduling surgery is not flexible and takes a long time. This may cause a not so optimal reconstruction with complications being.  
It is important to mention that there are many challenges from applying Artificial Intelligence (AI) techniques to medicine. These challenges are mainly due to the lack of interpretation and repeatability of the results generated by Machine Learning (ML), which may constraint their application. In all areas, systematic and comprehensive high-level data must be created. With ongoing endeavors to address these concerns, as well as raising consciousness of the aspects required to validate ML practices, the implementation of ML can encourage data-driven decision-making and have the prospective to speed up the process and decrease failure rates in drug discovery and development [29]. Thus, an effective scheme that involves cutting edge technologies is imperative to achieve this goal.