In recent years, deep learning and machine learning have been utilized in healthcare, bringing profound changes to traditional healthcare, especially influencing innovations in E-health and M-health. Additionally, with the rapid development of 4G/5G, the popularity of Internet of Things (IoT)-based wearable medical devices has increased the success of E-health and M-health. However, there are still some challenges in providing a better service and user privacy preservation, and E-health and M-health present an unprecedented technical challenge in terms of data collection, processing, storage, transmission, analysis, and sharing. For example, while the collection and analysis of medical data can effectively help researchers to extract case feature sand to achieve the objectives of precision medicine, it may cause privacy issues for patients.

Meanwhile, the collection and use of medical data has received unprecedented attention by researchers, especially with regards to improving the quality of medical services while protecting the privacy of users. In recent research, deep learning and machine learning have been shown to have the ability to solve complex issues accurately and efficiently in various research fields, and can effectively solve challenges and issues in E-health and M-health, especially for improving the quality of service and the experience of medical users. As this requires deep and machine learning and big data-oriented algorithms, models, systems and platforms to support the analysis, use, interpretation, and integration of various health or medical data need to be developed.

This Special Issue focuses on deep learning and machine learning for privacy preservation in E-health and M-health, as well as related works. We invite authors to contribute interdisciplinary papers on deep learning and machine learning in healthcare. Original research and review articles are both welcome.

Potential topics include but are not limited to the following:

• Deep learning and machine learning (DL/ML) for E-health and M-health
• DL/ML techniques and approaches for precision medicine and personalized healthcare
• Security, trust, and privacy in E-health and M-health
• Algorithm optimization and improvement in DL/ML
• Medical big data analysis with DL/ML
• Image translation in medicine
• Health informatics domains
• IoT-based wearable medical technology
• Health system solutions using Internet of Medical Things (IoMT)
• Related technology with E-health and M-health
• Blockchain and privacy preservation in E-health and M-health
• E-health and M-health related software, equipment, and technology
• Computational intelligence in E-health and M-health
• E-health and M-health informatics and information management systems
• Energy management and optimization for E-health and M-health
• Patient diagnostics via E-health and M-health solutions
• Electronic health records storage and transmission
• Big data analytics, machine learning, and visualization techniques for E-health
• Advanced mathematical models for connected health care