## 5 Edge AI for Early Health Prediction
### Early Detection of Chronic and Infectious Diseases  
Several studies have investigated the use of AI for the early detection of chronic diseases such as cardiovascular disease, diabetes, cancer, and infectious diseases [14; 15; 45; 46; 47; 48; 49; 50; 51; 52]. These research efforts highlight the ability of AI to significantly improve public health outcomes by allowing health professionals to detect chronic diseases in their early stages when interventions are most effective. By analyzing data from multiple sources, AI al  
Figure 4: Edge AI-based architecture for early health prediction.  
gorithms comprehensively summarize a person's health status and predict how health conditions will evolve. This allows healthcare providers to develop tailored interventions and therapies to improve an individual's health and slow disease progression.  
**Cardiovascular Disease**: AI algorithms process data derived from wearables, electronic health records, and other sources to reveal deviations in a person's heart rate and activity metrics [53]. These discrepancies indicate the onset of cardiovascular disease. In [50], the authors describe recent developments in digital health applications tailored to cardiovascular disease. They focus on methods for detecting, diagnosing, and predicting cardiovascular disease using AI models based on data from wearables. They summarized the literature on wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the prevailing AI approaches for modeling and prediction using data collected from sensors such as wearables. They asserted that machine learning and AI-driven models outperform traditional statistical methods in predicting cardiovascular disease. Further confirmation comes from a separate study, as stated in [54], claiming that machine learning models are the first choice for prediction and classification measures in heart disease.  
**Diabetes**: AI algorithms are able to decode data from electronic health records and wearable devices. These algorithms can evaluate changes in a person's blood glucose levels and activity parameters that may indicate the onset of diabetes. This idea is supported by the study in [55], in which the authors meticulously explain machine learning and AI-based methods capable of detecting and self-treating diabetes mellitus.  
**Cancer**: AI algorithms skillfully analyze medical imaging and electronic health records to detect health changes that indicate the presence of cancer. This is illustrated in [48], in which the authors explore the role of AI in the digital pathology of breast cancer, outlining both the current state and the challenges ahead.  
**COVID-19**: AI holds great promise in several areas of COVID-19 data reviews. Foremost is its role in developing predictive models for COVID-19 diagnosis and prognosis. Using large datasets of COVID-19 patient data, AI algorithms have been refined to identify predictive patterns and attributes associated with disease severity, mortality, and important outcomes. The application of AI also extends to accelerating medical image analysis, including chest X-rays and CT scans, which critically aids COVID-19 patient diagnosis and monitoring. The authors in [56] discuss the development of a federated learning model called EXAM, which predicts the oxygen requirements of COVID-19 patients using data from 20 institutions worldwide. EXAM achieved a mean AUC (Area Under The Curve) of 0.92 in predicting outcomes within 24 and 72 h, demonstrating an improved average AUC and a broader applicability in contrast to models trained at only one site. FL facilitated rapid collaboration without data sharing and enabled a model that could be generalized across heterogeneous datasets, demonstrating the potential of FL in healthcare.