Digital public health is the integration of digital technologies and artificial intelligence (AI) into public health. It can enhance the quality of healthcare, by increasing safety and security of patients, data protection, and reducing administrative and operating costs. The main goal of digital public health is to share data or information between clinicians, devices, and patients in a more efficient way.

Healthcare providers manage and maintain patient records, Hospital Information Systems, medical equipment, medication errors, and so on. However, there are substantial challenges that constrain the extent and scale of this digital incorporation in numerous public health domains. Nontechnical challenges include health equity, policy and governance, quality of evidence, resource gaps, and ethics. Technical challenges involve unreliability of available data, lack of clear standards, workforce capacity gaps, fragmented and unsustainable systems, and infrastructure gaps. The development of wearable and Internet of Things (IoT) devices for digitization of healthcare raises some ethical issues while collecting data, such as privacy and security concerns. AI has already accomplished some outstanding achievements in a range of healthcare-based tasks but its explainability is an important issue due to the frequent use of “black-box” models, most prominent when using deep learning in digital public health. Adaptive and evaluative research is needed into the standardization, ethics, intersectoral collaboration, economic investment, political commitment, legality, public engagement, regulatory frameworks, transparent communication, and workforce capacity within digital public health.

The aim of this Special Issue is to identify solutions for overcoming the different challenges confronted when integrating AI and digital technologies into the public health sphere. We aim to collect research on diverse topics in digital technologies and big data analytics such as lifelogging, multimedia, medicine, pervasive health, accessibility, health, human-computer interaction, and psychology. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Health Education
• Interpretability and explainability of AI models in public health decisions
• Visualization and fusing of health and wellness
• Hospital Management System
• Complex health monitoring
• Multimedia analysis and Brain-computer interaction
• Healthcare Data analysis and Management
• AR/VR/MR for exploring healthcare data
• Health behavior analysis
• Personal health care records analysis and monitoring
• Health-associated event detection in big data
• Long-term health monitoring and analysis
• Monitoring of health associated activities
• Ethical issues regarding collection of data
• Retrieval of health data