Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review

Abstract

Healthcare is rapidly evolving with the integration of machine learning (ML) and edge computing, which enables real‐time data processing and improved patient care. Edge computing plays a critical role by reducing latency and enhancing data privacy, especially in patient monitoring systems. However, limitations such as device resource constraints and security issues persist. This study presents a systematic literature review (SLR) on using ML and edge computing in healthcare, identifying key benefits, challenges, and research trends. This SLR aimed to identify key benefits, challenges, and current research trends. We sourced relevant studies from databases such as IEEE Xplore, ScienceDirect, ACM Digital Library, and so forth. We applied inclusion and exclusion criteria. We also used the snowballing technique to find more relevant studies by checking selected papers' reference lists, ensuring we did not miss any important ones. Finally, 37 papers were selected and analyzed for their methodologies, algorithms, tools, frameworks, data sources, limitations, motivations, and challenges. Findings show a broad use of ML methods such as support vector machines, clustering, and deep learning, with a strong emphasis on data privacy and model performance; many studies employed federated learning and privacy‐preserving techniques to support real‐time decision‐making. Overall, ML and edge computing integration promise to transform healthcare, though challenges remain. Future research should address resource limitations, enhance ML models for edge environments, and develop standardized protocols. This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Cloud Computing