Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 3rd Edition

Topic Information

Dear Colleagues,

Since the COVID-19 pandemic, we have witnessed a profound and lasting increase in scientific and societal interest in the development and application of Artificial Intelligence (AI) in the health domain. The pandemic served as a pivotal a turning point, exposing the fragility of health systems while underscoring the critical importance of data-driven, scalable, and adaptive solutions for managing complex public health challenges.

In this context, AI research has become strategically vital in the evolution of health systems and is inextricably linked to the broader development of digital health. This includes the collection, monitoring, integration, and management of health-related data, as well as the optimization of hospital infrastructures and interconnected governmental information systems. Technologies such as wearable monitoring devices, big data analytics, and robotics have already demonstrated their value in supporting diagnosis, treatment, and care pathways.

At the same time, the scope and capabilities of AI have expanded significantly beyond traditional machine learning and pattern recognition. Recent advances—particularly in generative AI and large language models (LLMs)—are transforming how knowledge is produced, accessed, and applied. These technologies are reshaping scientific communication, clinical documentation, citizen engagement, and decision-making processes, enabling new forms of interaction between individuals, professionals, and health systems.

Simultaneously, the emergence of computational modeling and digital twins offers innovative perspectives for simulating biological processes, patient-specific conditions, and even population-level dynamics. These tools enable the exploration of “what-if” scenarios, supporting prevention strategies, policy design, and system-level resilience.

Artificial intelligence applications continue to permeate a wide range of domains, including: organ, functional tissue, and cellular diagnostics; care robotics assisting in interventions, rehabilitation, and communication; biomedicine, from genetics to advanced modeling; and precision and personalized medicine. These established areas remain fundamental, especially when integrated with new AI paradigms and translated into scalable solutions.

A statement attributed to Henry Ford reminds us that “real progress happens only when the advantages of a new technology become available to everybody”. In this sense, the consolidation of AI technologies in health should aim to generate benefits for all stakeholders—from institutions to professionals, and ultimately to citizens and patients—promoting accessibility, inclusiveness, and equity of care.

Within this evolving landscape, public health plays a central and unifying role. AI has the potential to profoundly impact the following:

The prevention of disease onset at both individual and population levels;
The provision of personalized care and assistance within community and system contexts;
The understanding of disease trends and the influence of biological, environmental, and behavioral determinants;
The organization and optimization of healthcare services, including diagnostic and decision-making processes;
The design, implementation, and evaluation of public health policies.

Through AI, big data can be leveraged to predict diseases and identify risk patterns across populations, enabling early interventions and targeted prevention strategies. At the same time, wearable and connected technologies allow continuous monitoring of individuals, supporting adaptive and personalized care pathways that can be scaled to population-level insights.

Furthermore, the synergy between AI immersive technologies—such as virtual reality and augmented reality—is pioneering new forms of digital health services, including remote care, training, rehabilitation, and simulation environments. These innovations contribute not only to clinical practice but also to public health preparedness and education.

However, the rapid expansion of AI also raises critical ethical, legal, and societal challenges, including issues related to transparency, accountability, bias, data protection, and governance. In addition, there is an increasing need for interdisciplinary education and training to equip professionals with the skills required to apply these technologies effectively and responsibly.

This Topic provides a comprehensive platform to explore both consolidated applications and emerging frontiers of AI in health, with a particular emphasis on public health perspectives, while remaining open to interdisciplinary and clinical contributions that demonstrate clear implications for population health, health systems, and societal impact.

We invite contributions that address the research, development, validation, and application of AI in current and future scenarios, fostering dialogue between technological innovation and public health needs.

In this Topic, original research articles, reviews, commentaries, opinions, viewpoints, communications, and brief reports are welcome. Research areas may include (but are not limited to) the following: