# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 1 Introduction  
Edge computing is a distributed computing model that processes data closer to where it is generated [1]. Unlike traditional cloud computing, which depends on centralized data centers, edge computing brings computation and data storage nearer to the data sources, such as Internet of Things (IoT) devices or smartphones [2]. The primary benefit it offers is reduced latency and enhanced overall system performance. This improvement makes it possible for numerous applications that require real-time data processing or decision-making to function effectively in critical environments [3]. The widespread adoption of IoT devices, equipped with sensors, software, and network connectivity, has significantly advanced data collection and analysis. These devices are capable of gathering, processing, and transmitting information for further analysis [4]. The global count of IoT-enabled devices is projected to exceed 29 billion by 2030, nearly tripling the 9.7 billion devices recorded in 2020 [5]. Firstly, this growth highlights the mass adoption of IoT devices in our day-to-day life in a vast number of sectors. Secondly, it is accompanied by an exponential growth of generated data volume, leading to creating bottlenecks in the current centralized cloud-based architecture [3]. The increased bandwidth usage together with latency andprivacy concerns required research and development efforts for providing innovative edge computing technologies [6]. In this context, domains such as Ambient Assisted Living (AAL) or healthcare can greatly benefit from the IoT and edge computing advancements. AAL focuses on bringing aid and personalized assistance to daily activities and offering proactive interventions to enhance the quality of life for individuals [7]. The applications in the current state of the art vary from basic sensing applications such as fall detection, medication monitoring, and remote follow-up, to more complex tasks such as personalized care and cognitive decline management [8]. The integration of edge computing technologies in healthcare systems enables IoT wearable devices that are equipped with a diverse range of sensors to continuously monitor a wide range of signals and detect anomalies, which improves the response time of interventions and can help prevent serious health problems [9]. Another advantage for healthcare professionals is the optimization of their workload by allowing them to focus on critical cases that require immediate intervention. Over the past years, user trust in centralized solutions has started to decline due to various personal data leaks and security breaches. While centralized cloud solutions have been a preferred choice for developers due to ease of use and powerful processing capabilities, they have begun to be a prime target for cyber-attacks. Edge computing addresses the problems of privacy and security in sensitive environments as well [10]. Processing recorded data locally, edge-enabled applications reduce the need to transfer sensitive data over networks, eliminating the possibility of unauthorized access and data breaches. This paper aims to explore the current state of the art of edge computing in the healthcare domain, the current challenges, innovations, and its potential for future developments. To conduct this study, we have used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology [11], with relevant research questions, specific criteria for inclusion and exclusion of research works, and a reference interval on most relevant databases. PRISMA not only ensures a rigorous and transparent way of performing our literature review but also offers the readers the opportunity to conveniently gain relevant information from the current state of the art in a research field. We have focused our review on three main edge computing research problems and classified the research works considering privacy and security, optimization techniques, and edge task offloading.