# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 2 Edge Computing Overview
### Use Cases and Application Domains  
There are several use cases for edge computing technologies that are deeply analyzed in the research literature [1; 2; 3; 4] (see Figure 2). Industrial IoT (IIoT) platforms and systems can take advantage of edge computing since they require low-latency communication that can be achieved through processing data locally. At the same time, IIoT systems leverage multiple data streams and spatiotemporal correlations for efficient processing and are focused on data security and efficiency by reducing the need for constant data transfer to cloud servers. For computer vision, most applications focus on object detection and image processing, which can benefit significantly from edge computing [4; 16]. By processing data locally at the network edge, bandwidth is reduced without the need to upload sensitive data and this motivates the approach from a scalability perspective as well. Edge devices for smart utility services are capable of reporting finer-grained energy consumption details directly to user's mobile devices and offering suggestions on choosing the most economical energy source and optimal times to operate high-consumption home appliances to minimize usage and cost [20]. For Augmented Reality (AR) and cognitive systems, deep learning can be utilized to detect objects in the user's field of view and apply virtual overlays. By processing these tasks on edge devices, AR systems can significantly reduce the motion-to-photon latency, enhancing user experience by ensuring timely and responsive virtual overlays. For example, splitting the data into time-sensitive and non-time-sensitive, offloading the first category to edge devices and handling the latter on fog or cloud layers, improves the system's overall response and effective assistance in real time [21]. For network management, Intrusion Detection Systems (IDSs) actively respond to detected attacks by blocking malicious packets by using deep learning algorithms, thus having the ability to analyze large volumes of network traffic and identify anomalous patterns indicative of potential threats in an ultra-low-latency environment [22]. Another application is in-network caching, which reduces the need for repeated data transfer over long distances, thereby lowering latency and network concentrations [4]. Edge computing plays an important role in Natural Language Processing (NLP) applications, particularly for voice assistants on mobile devices and conversational Als. These assistants use on-device processing to detect wake words which trigger further processing and use cloud connection only when necessary. Edge-based  
Figure 2: Edge computing use cases. NLP solutions address latency and privacy concerns, thereby enhancing user experience, efficiency, and data privacy [4]. In healthcare, edge computing can help to detect, predict, and prevent health problems by dynamically deploying Artificial Intelligence (AI) algorithms across edge devices that will support low latency, mobility, location awareness, and privacy considerations [7; 8]. Edge computing is driving the transformation of the healthcare sector by bringing data processing and analysis closer to the point of data generation, whether in hospitals or patients' homes (see Figure 3). Bringing computation and data storage closer to the patient will offer various benefits to the entire sector:  
* allows for real-time decisions and enables for immediate response from professionals. * reduces the risk of data breaches by processing and storing personal information at the edge. * bandwidth and cost reduction by transmitting only the mandatory information to centralized entities. * allows AI model deployment closer to the patient, enables personalization of AI models, and the prediction of health issues based on real-time data from the monitoring devices. Processing at the edge enables privacy-preserving predictive analytics on a large scale and can help to identify trends in public health, predict outbreaks, and enable early detection of health issues.