JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic exposed systemic vulnerabilities in public health infrastructure, underscoring the urgency for innovation in disease surveillance and emergency response. Artificial intelligence (AI) has emerged as a promising tool to enhance the accuracy, efficiency, and scalability of public health interventions. Yet, there remains limited understanding of how AI has been applied in real-world infectious disease control, and who is contributing to its development and implementation.

Objective:

This scoping review aims to map current applications of AI in public health practice for infectious disease control since 2020. Specifically, it examines (1) the types of AI tools in use, (2) their purposes and implementation contexts, and (3) the professional and institutional actors leading these efforts, including the role of nurses.

Methods:

Using the Joanna Briggs Institute’s Population, Concept, Context (PCC) framework, we conducted a structured search in Ovid MEDLINE®, guided “5Cs” framework for health emergency preparedness from the World Health Organization (WHO). Inclusion criteria focused on English-language, peer-reviewed studies from 2020 onward that used AI tools for infectious disease control within real-world public health practice. Non-original articles and simulation-only studies were excluded.

Results:

Out of 600 screened studies, 10 met the inclusion criteria. Two major AI types were identified: machine learning (ML) algorithms and language-based tools such as chatbots and large language models (LLMs). ML tools supported outbreak detection, risk stratification, and resource allocation, while language-based tools promoted health communication, particularly around immunization and HIV prevention. Most studies were conducted in low- and middle-income countries and used national datasets or surveillance systems. Despite nurses comprising half of the global health workforce, no nursing-affiliated authors were found among first or corresponding authors, and none were represented in the broader authorship of included studies.

Conclusions:

AI technologies are increasingly applied to support public health response to infectious diseases, with applications ranging from predictive analytics to real-time public engagement. However, adoption remains limited in scale, scope, and professional diversity. The near-total absence of nursing participation in AI-related public health research is particularly striking and represents a missed opportunity for inclusive innovation. Strengthening implementation research and advancing informatics education among nursing professionals are critical next steps to ensure AI tools reflect the realities of public health practice and promote equitable outcomes.