## 3 AI and Edge Computing Synergy in Healthcare
* _Vital sign monitors_: Vital sign monitors are medical devices that monitor various physiological parameters such as heart rate, blood pressure, and oxygen saturation. These devices can be used in hospitals, clinics, or at home and allow real-time monitoring of the patient's health status. Using edge computing, these devices can analyze vital signs in real time and alert healthcare providers to anomalies [40; 41]. Edge computing facilitates remote patient monitoring and enables healthcare providers to monitor health statuses in real time and detect changes that signal the possible onset of a chronic disease [42; 43]. It has the potential to significantly improve healthcare outcomes by enabling healthcare providers to detect chronic diseases at the onset when intervention is most effective. By processing data instantly and protecting sensitive information, edge computing can amplify public health outcomes by equipping healthcare providers with the insights needed to make informed judgments about an individual's health status. Edge computing has been used to predict the spread of infectious diseases through real-time analysis of data from multiple sources, including wearables, electronic health records, and social media. This method enables healthcare professionals to detect infectious disease outbreaks at an early stage, monitor the transmission of such diseases, and take proactive measures to contain their spread [43]. The framework proposed in [44] uses heart rate and sleep data from wearable devices to predict the progression of COVID-19 outbreaks  
Figure 2: Common wearable devices to monitor several health parameters. in different countries and cities. The model claims that early detection and warning of epidemics can be facilitated by such predictive models. Edge computing offers a variety of advantages that make it particularly suitable for predicting health conditions in their early stages:  
* _Real-time data analysis_: Edge computing offers the ability to analyze large datasets in a timely manner, thereby detecting changes in a person's health status in virtually real time. Timely detection of potential health problems is critical for early health prognosis, enabling health professionals to identify potential health problems before they worsen and become more difficult to treat. * _Shortened latency_: By processing data at the periphery of the network and closer to its source, edge computing effectively reduces the latency that typically occurs when data are transmitted to a central data center for analysis. Incorporating edge computing technology can provide significant benefits to healthcare providers by enabling rapid data analysis and informed decision making regarding a patient's health status. * _Improved data security_: Edge computing is a notable advancement that aims to protect sensitive data from security breaches through localized data processing at the network perimeter. Limiting the need for extensive data transmission over the internet provides a critical solution for early health prognosis. This attribute plays a central role in maintaining the confidentiality of an individual's private health information, ensuring that their information is used only to promote their well-being. * _Cost efficiency_: The increased cost efficiency of edge computing makes it particularly beneficial for healthcare providers, as they have the ability to leverage artificial intelligence and other cutting-edge technologies to improve healthcare outcomes. * _Scalability_: Edge computing is a highly scalable framework, making it a suitable choice for health predictive initiatives that operate at scale. By performing data processing at the periphery of the network, edge computing can handle large amounts of data, enabling real-time monitoring of the health status of multiple individuals simultaneously.