## 5 Edge AI for Early Health Prediction
### Stakeholders and Architecture  
The synergy of AI and edge computing promises to reshape healthcare by empowering healthcare providers with instant data and insights to drive early detection and prevention of diseases. Nevertheless, the full realization of these technologies depends on sustained investment in research and development, careful management of ethical and regulatory issues, and strategic management of challenges and constraints inherent in these areas. These obstacles are discussed in Section 7. An Edge AI-based early health prediction system, such as the one depicted in Figure 3, involves several key stakeholders, including patients, healthcare providers, technology companies, governments and regulators, and research institutions. Each of these stakeholders plays an indispensable role in developing and implementing Edge AI-enabled systems tailored to early health prediction. Working together can help ensure the systems' efficiency, safety, and ethical behavior, with significant benefits for patients and healthcare providers. _Patients_ are the primary users of Edge AI systems for early health prediction and are responsible for collecting and transmitting data to healthcare providers. _Healthcare providers_ are tasked with reviewing the data collected from their patients and using the information to make informed judgments about patient care. This includes the processes of diagnosing diseases, developing appropriate treatment plans, and monitoring patient well-being over time. The task of developing and implementing Edge AI systems for healthcare is being undertaken by _technology enterprises_. This includes the development of sensors, devices, and AI algorithms in conjunction with the execution of edge computing and cloud infrastructure. _Governmental and regulatory entities_ assume a central role in formulating benchmarks and guidelines for the use of Edge AI in healthcare. This includes ensuring compliance with privacy and security laws and protecting patients from potential harm. Finally, _Research Institutions_ play a key role in advancing the field of Edge AI and developing new technologies for early health prediction. This includes participating in research and development projects, conducting clinical trials, and disseminating research findings through scientific publications. As shown in Figure 4, an Edge AI-based architecture for early health prediction typically consists of several key components, including:  
1. _Sensors and Devices_: The foundation of AI-driven edge systems for early health prediction depends on the use of sensors and devices that collect and transmit patient data. These devices typically include wearable devices such as fitness trackers and smartwatches and medical devices such as ECG monitors and glucometers. 2. _Edge Computing Devices_: Edge computing devices are responsible for processing and analyzing data at the point of origin. They include a variety of components, such as edge gateways, edge servers, and edge routers. 3. _AI Algorithms_: AI algorithms play a central role in carefully examining data collected by sensors and devices and facilitating the detection of latent health anomalies. By leveraging historical patient data, these algorithms can be trained to improve their accuracy in detecting patterns and trends indicative of an imminent health problem. Figure 3: Main stakeholders of an Edge AI-based system for early health prediction. 4. _Cloud Platforms_: Cloud platforms serve as repositories for data acquired by Edge AI systems. The information thus obtained can be used for deeper analysis and modeling. Subsequently, these data are even used to refine AI algorithms through training. 5. _User Interfaces_: User interfaces allow patients and healthcare providers to access the data and insights generated by Edge AI systems. Accessible through a variety of media, including desktop computers, mobile devices, or other internet-connected platforms, these interfaces play a critical communicative role. 6.