Artificial intelligence (AI), big data, blockchains, and the Internet of Things (IoT) are new disciplines, involving new technologies that are revolutionizing the world. Artificial intelligence gave birth to emerging subdisciplines, namely machine learning and deep learning, which created interesting dimensions of intelligence and gained ground in various fields of application. Artificial intelligence generally handles a big volume of data. IoT systems may also produce an enormous volume of data as data streams that should be treated in real-time. This big data is generally collected for further use in huge data centers. Thus, big data is inherent to intelligent systems as well as IoT systems. Blockchain and trustworthy systems pave a new way for innovating a wide range of application scenarios due to their beneficial characteristics of being tamper-proof, transparent, trustworthy, and protecting privacy. Nowadays, big data and blockchain-related techniques, intelligent systems, and IoT provide huge support for data processing, exploration, and communication performed in health and the environment.

AI is essentially the core of intelligent systems. AI is widely used in the fields of computer sciences, healthcare, bioinformatics, etc., to facilitate accurate analysis, monitoring, and decision-making. These AI applications have revolutionized the health/environment informatics domain. Several advanced applications cover various fields of healthcare ranging from e-healthcare to personalized healthcare. For instance, electronic medical records (EMRs) and EEG-based systems.

This Special Issue focuses on the latest progress and future trends related to the application of intelligent systems, big data, EEG, and the Internet of Things in health and the environment. The Special Issue covers various problems faced when using intelligent systems, big data, blockchains, and the Internet of things in health and the environment.

Potential topics include but are not limited to the following:

• E-Health
• IoT-driven monitoring health of people
• IoT-based monitoring of the environment
• Integration and exploration of biomedical and industrial data
• Artificial intelligence-driven analytics of data
• Scalable solutions for environmental and health informatics
• Machine learning and deep learning for healthcare
• Machine learning and deep learning for environment protection
• Hybrid intelligent models and applications for healthcare applications
• Big data analytics in healthcare
• AI-driven data analytics for healthcare and the environment
• Privacy-aware data analytics
• AI-based signal and image processing applied to health and the environment
• EEG signals and systems