###### Abstract
Within the context of Edge AI's flourishing significance in healthcare, it is essential to establish a comprehensive understanding of its applications and implications. Several prior review papers have explored facets of artificial intelligence (AI) in healthcare, with a predominant focus on specific diseases, imaging modalities, or AI techniques. Herein, we delineate how our work distinguishes itself from these review papers by taking a broader and more inclusive perspective. A paper by [PERSON] et al. [17] offers an extensive survey of deep reinforcement learning (DRL) in the context of medical imaging and radiation therapy. It covers DRL's basic concepts and algorithms and discusses its applications in lesion localization, classification, registration, segmentation, and treatment planning. [PERSON] et al. [18] provide a primer on the steps involved in developing and deploying deep learning models primarily tailored to radiology applications. It covers data collection and preprocessing, model design and training, model evaluation and validation, model deployment and integration, and model monitoring and maintenance. Furthermore, [PERSON] et al. [19] reviewed deep learning principles and their applications in various biomedical domains, such as genomics, proteomics, metabolomics, microbiomes, medical imaging, drug discovery, and precision medicine. They also discussed the challenges and future directions of deep learning in biomedicine. Federated machine learning and its use in disease prediction was covered by [PERSON] et al. [20], who offered a comprehensive review of federated machine learning (FML) within the healthcare context. The paper also surveys the recent applications of FML in various disease prediction tasks, such as cancer, cardiovascular disease, and diabetes prediction. In another work, [PERSON] et al. [21] conduct a systematic literature review of the state-of-the-art smart wearables that can detect and monitor cardiovascular diseases (CVDs), the leading cause of death worldwide. They focus on the types, features, and applications of smart wearables, such as smartwatches, smart bands, smart rings, and smart glasses, that can measure various physiological signals, such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), and heart rate (HR) signals. In summary, our work stands out through its exploration of Edge AI applications in healthcare. It accentuates the potential impact of Edge AI on public health, early prediction of chronic diseases, and its seamless integration within established healthcare systems. Although the aforementioned review papers delve into specific niches within healthcare and AI, our work serves to provide a holistic understanding of the implications of Edge AI technologies in healthcare, emphasizing the necessity of a comprehensive grasp of Edge AI's role in healthcare settings. Our contributions can be summarized as follows:  
1. We describe the synergy between AI and edge computing in healthcare to create opportunities for healthcare providers. These opportunities include cutting-edge tools that provide instant data and deep insights. These tools help accelerate disease detection and create tailored treatment plans. The plans consider the unique characteristics and needs of individual patients. 2. We review the various applications of Edge AI in the early prediction of health issues and the detection of chronic and infectious diseases. 3. We scrutinize machine learning and deep learning models instrumental in early health prediction and detection of chronic and infectious diseases. 4. We review the various applications of federated learning for disease and mortality prediction and highlight the critical dimension of privacy in early health prediction. 5. We identify and elaborate on the challenges faced by Edge AI in the healthcare domain, such as privacy, data accuracy, model bias, interoperability, and integration with existing health systems.