In recent years, the world has seen rapid progress in the development and use of digital technologies, which has had a significant impact on the lives of the global population. The application of novel technologies within health systems has the potential to benefit human health and to produce a huge amount of digital data on public health.

This enormous amount of digital data on public health may greatly complement and expand traditional sources of clinical data, capturing the richness and granularity of individual behavior, the confluence of factors affecting behavior, as well as the individualized evolution of behavior. By revealing the digital markers of health and risk behavior, digital data on public health could contribute to discovery science, address issues of bias, and promote the clinical tracking of disorders over time. Artificial intelligence (AI), combined with big data, can perform automated/case-based reasoning, constraint processing, deep learning, and deep reinforcement learning. Advances in AI and big data analysis have created unparalleled opportunities to assess and modify health behavior, and enable scientists to understand and improve health behavior and health outcomes.

This Special Issue intends to provide a platform for researchers, health practitioners, and policymakers to apply state-of-the-art AI techniques to digital data on public health to obtain critical insights. We especially welcome original research and review articles that address digital health data-driven intelligent approaches, as well as their applications in health care and public health.

Potential topics include but are not limited to the following:

• Analyzing mental and emotional health in the context of digital data on public health
• Big data modeling and machine learning for e-health
• Bias analysis based on big data methods for e-health
• Reducing bias in digital public health-based AI systems
• The application of digital data in public health
• Collection and management of digital data on public health
• Data-driven empirical analysis for health behavior and psychopathology
• Public health interventions and disaster risk reduction
• Other research related to data-driven intelligence for digital public health