Machine Learning (ML) plays a significant role in the development of smart and automated healthcare strategies. Smart technologies (preferably wearable devices) are integrated into conventional medicine, including the diagnosis, monitoring, and treatment of illness. Wearable devices are considered as a paradigm shift to reshape the healthcare domain, specifically in smart healthcare. However, the collection of data and training processes of various machines are the pivotal parameters to build smart IoMT based healthcare systems. Accuracy and precision of these systems are based on the effective utilization of data collection and ML approaches, which enable the collection and processing of data to make more informed, automated decisions. Therefore, the integration of new learning and assessment methods is an emerging trend in IoMT based applications.

However, the vast use of these devices for next generation IoMT applications generates massive amounts of data, which can significantly deteriorate computing efficiency. Thus, a challenging issue to the research community is how to improve performance of ultra-fast computing in IoMT through ML techniques. Due to rapid development, several AI techniques are entering a more mature phase, but it is essential to investigate the trade-off between performance and the requirements of next generation applications. Furthermore, IoMT and AI-enabled monitoring systems remain in their infancy in the healthcare field, and researchers therefore need to explore their potential whilst considering the challenges, such as prediction and cure of various complex diseases. The main theme of AI and ML methodologies is to mimic the behavior of humans in resolving real world problems effectively, which are related to almost every domain, including healthcare. These techniques have become smarter and more efficient with recent advancements including wearable devices. A combination of these two techniques surely will lead to the development of real-time automated systems involving diagnosis, prediction and decision support systems, etc.

This Special Issue will cover the recent novel advances in enabling technologies for IoMT and ML-based healthcare strategies. The objective is to attract high-quality original research and review articles that promote research and reflect the most recent advances in addressing the thorough analysis and sustainability issues of medical data, specifically neuro or other domains, for smart healthcare systems. We would be grateful to researchers to provide their state-of-the-art technologies and ideas covering all aspects of human-related diseases, evaluation of medical data, and sustainability solutions for healthcare applications.

Potential topics include but are not limited to the following:

• Evaluation and diagnosis process of diseases
• Machine Learning and its applications in the development of precise healthcare strategies
• Internet of Medical Things (IoMT) and its application in the development of precise healthcare strategies
• Precise and accurate medical data evaluation systems
• IoMT and AI based system for the development of decision support system
• Biomedical and AI-based system to assist doctors and practitioners in the healthcare domain
• Networking architectures and platforms for IoT-based healthcare systems
• Machine learning based secure and sustainable healthcare systems
• Effects of various medicine and state-of-the-art approaches on human body
• Integration of big data and ambient intelligence with IoT-based healthcare systems
• Sensors, wearable devices and energy efficient protocols for IoT and ML-based healthcare systems
• Applications of teaching methodologies in the healthcare of an individual
• Medical or behaviors related assessment systems both traditional and automated
• Machines learning and bioinformatics in healthcare systems
• Technology convergence and standardization issues for IoMT-based systems
• Mathematical and business model for IoT and ML-based wireless networks and Internet of Things