Managing healthcare devices are one of the critical research subjects that have attracted multidisciplinary research groups. Many academics are interested in deploying new emerging technologies for health, primarily based on Artificial Intelligence (AI), Computational Intelligence, Internet of Things (IoT), named data networking, and Fog Computing. The seamless integration of heterogeneous healthcare devices has resulted in service enhancement for several medical application areas. In addition, these technologies provide limitless scalability, escalated productivity, and a surplus of additional paybacks. This also enables the realization of connecting the physical world with cyberspace, resulting in a sophisticated Internet of Medical Things.

IoMT domain, while presenting its diverse application areas, has urged researchers to investigate the current infrastructures, processes, tools, and technologies to accommodate massive data aggregation and other related events. As there is a vast amount of data generated by these IoMT based systems, the insights and resulting decisions from the generated data enable us to make better decisions and facilitate end users by applying machine learning techniques. The machine learning models and the data generated by the IoMT systems act as sources for continuous improvement and service enhancement in their respective application areas. Moreover, the proven effectiveness of machine learning has reduced the need for human intervention in significant decision-making. As the IoMT based systems continue to expand their utility in various aspects of future smart cities, increasing algorithms and models are required to keep up with the requirements of multiple scenarios.

In this Special Issue, researchers from academia and practitioners from the industry are invited to submit their cutting-edge original research and review articles on machine learning-based methods and techniques for performing data analytics in IoMT systems. This Special Issue aims to address advances in machine learning techniques for IoMT systems and improve services based on data analytics, covering topics ranging from enabling technologies to emerging applications and, importantly, industrial experiences.

Potential topics include but are not limited to the following:

• Autonomic computing approach for IoMT systems
• Big data and machine learning techniques for IoMT
• Deep learning models for semantic web & linked open data based applications in IoMT
• Real-time analytics for Industrial Internet of Things (IIOT) as well as IoMT systems
• Machine learning models for trust and privacy-preserving in IoMT systems
• Data mining and statistical modeling for service improvement through IoMT
• Machine learning for energy management in the IoMT systems
• Resource Monitoring in IoMT for enhancement of Fault Tolerance
• Technology convergence and standardization issues in the IoMT systems
• Multi-Agent IoMT Systems
• Ontology and semantic knowledge for large-scale IoMT systems
• Evolutionary and bioinspired algorithms for Multi-Agent IoMT Systems
• Intelligent middleware solutions for large-scale IoMT systems
• Resource interoperability and social aspects of IoMT based systems
• Machine learning for emergency detection