Computational intelligence and artificial intelligence (AI) have a remarkable role in the development of smart healthcare systems by integrating smart technologies (preferably wearable devices) into conventional medicine, including the diagnosis, monitoring, and treatment of illness. Miniaturized devices are the building blocks and paradigm shift to reshape healthcare, thus data collection and machine learning are the pivotal parameters to build smart Internet of Things (IoT) and computational intelligence-based healthcare systems. Computational intelligence decisions, accuracy, and preciseness at different layers in IoT systems are dictated by computational intelligence techniques, which are responsible for data collection, the interconnection between devices and the internet, data processing, and decision-making, without human interaction. Therefore, super-fast computing with a preferred maximum accuracy level has always been crucial in the rapid development of IoT applications.

Development and realization of various machine learning and optimization methods, preferably with maximum accuracy and precision ratio, are the backbone of computational intelligence and smart IoT-based healthcare systems. Therefore, the integration of new learning and assessment methods is an emerging trend of great significance in next-generation of computational intelligence and IoT-based applications. These advancements in computational intelligence approaches can significantly enhance sustainability without compromising the quality of service. However, the vast use of these devices for next-generation IoT applications generates massive amounts of data, which can significantly deteriorate the computing efficiency. Thus, a challenging issue to the research community is how to improve the performance of ultra-fast computing in IoT through computational intelligence techniques? Due to the rapid development, specifically in advanced learning and optimization, several computational intelligence and AI techniques are entering a more mature phase, however, it is essential to investigate the trade-off between the performance of emerging computational intelligence techniques and the requirements of next-generation IoT and AI applications. Furthermore, IoT and AI-enabled monitoring systems remain in their infancy in the healthcare field, and researchers across the world, therefore, need to work hard to explore the potential of computational intelligence, IoT, and AI in the healthcare field, with consideration of various practical challenges such as prediction and cure of various diseases e.g. mental disorder (particularly schizophrenia, depression, and dementia), liver chronic infection, myocardial infarction (MI), viral infections (COVID-19), etc.

This Special Issue will cover the recent novel advances in enabling technologies for computational intelligence and machine learning-based healthcare systems. The objective is to attract high-quality research papers that promote research and reflect the most recent advances in addressing the security and sustainability issues of medical data, specifically neuro, for smart healthcare systems. We would be grateful to researchers from both academia and industry to provide their state-of-the-art technologies and ideas covering all aspects of security and sustainability solutions for healthcare applications. Review articles are also welcome.

Potential topics include but are not limited to the following:

• Computational intelligence and its applications in healthcare systems
• Artificial intelligence and its applications in healthcare systems
• Computational intelligence and AI-based systems for the development of decision support systems
• Computational intelligence and AI-based systems to assist doctors and practitioners in healthcare systems
• Networking architectures and platforms for IoT-based healthcare systems
• Machine learning-based secure and sustainable healthcare systems
• Applications and industrial trends in IoT-based wireless health
• Integration of big data and ambient intelligence with IoT-based healthcare systems
• Sensors, wearables devices, and energy-efficient protocols for IoT and machine learning-based healthcare systems
• Ontology and semantic knowledge for IoT-based wireless health
• Media cloud applications for healthcare applications
• Machines learning and bioinformatics in healthcare systems
• Technology convergence and standardization issues for IoT-based systems
• Mathematical and business models for IoT and machine learning-based wireless networks