## 3 AI and Edge Computing Synergy in Healthcare
There is immense potential in integrating AI into healthcare, but there are still challenges to overcome. Privacy and security issues, representative training data, accurate algorithms, and prospective clinical trials are critical aspects to consider. Robust solutions are needed to ensure data privacy, obtain diverse and high-quality training data, develop reliable algorithms, and conduct rigorous clinical evaluations to integrate medical AI into current workflows seamlessly. Healthcare could be significantly transformed by edge computing, which enables the timely examination of significant amounts of data derived from wearable devices, medical instruments, and other related sources in real time. 1. _Wearable devices_: Wearable technology, which includes devices such as fitness trackers and smartwatches, is evolving into a comprehensive data repository that contains various details about a person's physical movements, heart behavior, and sleep dynamics during nighttime hours (see Figure 2). The use of edge computing facilitates the instant processing of data originating from wearable devices. This accelerates the detection of changes in a person's health status that may serve as precursors to the onset of chronic disease. In a recent study by the authors in [21], several research papers were analyzed to examine the use of wearable devices in detecting and predicting cardiovascular disease. The study results suggest that wearable devices can effectively detect, predict, and treat cardiovascular disease. However, more research is needed to improve their use. In addition, in [34], the authors proposed a secure edge-computing-based framework for smart health systems. This framework focuses on real-time health monitoring and ensures data security and confidentiality through clustering approaches for anomaly detection and attribute-based encryption (ABE) for secure access to biosignal data. Experimental results of the proposed framework show an improved performance, an accuracy up to 98.5%, and data security. 2. _Medical Devices_: IoT sensors integrated into medical devices provide valuable data for early health prediction and continuous monitoring of patient's health conditions. Edge computing is a viable option for processing data obtained from medical devices such as blood glucose meters in real time. Processing data is an indispensable component that helps detect changes in a person's health that could indicate the development of chronic diseases such as diabetes [35]. Currently, there are several medical devices that use edge computing technology for their operations: * _Smart insulin pens_: Smart insulin pens, used to control blood glucose levels in diabetic patients, are advanced medical devices that integrate sensors and connectivity to track insulin doses and blood glucose levels and provide real-time feedback to patients [36]. These devices can process data in real time and provide patients with personalized insights through edge computing. * _Wearable electrocardiogram (ECG) monitors_: Wearable ECG monitors are medical devices used to monitor the patient's heart's electrical activity continuously. These monitors are attached to various areas of the body, such as the wrist, chest, or torso, and are aimed at individuals struggling with heart problems [37]. By integrating edge computing, these devices are able to analyze ECG data instantly. As a result, they are able to quickly detect irregular heart rhythms and other critical abnormalities, warranting immediate medical intervention. * _Smart inhalers_: Smart inhalers are devices designed to treat asthma or chronic obstructive pulmonary disease (COPD). They are equipped with sensors and connectivity features that allow them to track medication use and provide feedback to patients [38; 39]. The use of edge computing in smart inhalers has further enhanced their functionality by enabling real-time data processing and personalized patient insights.