JMIR Publications

Background:

Artificial intelligence (AI) is increasingly transforming multiple sectors, including public health. Recent years have witnessed a substantial increase in research on integrating AI into public health systems, reflecting growing interest in its potential to improve surveillance, diagnostics, policy development, and health outcomes.

Objective:

This study aims to identify how AI has been applied in public health, examine its impact on the field, and determine future research directions at the intersection of AI and public health.

Methods:

A systematic literature review was conducted to analyze existing scholarly research on AI applications in public health. Relevant studies were collected, screened, and categorized to identify dominant research themes and gaps in the literature.

Results:

The analysis revealed five primary thematic clusters within the literature: (1) AI model development and performance, (2) public health threats and outbreak management, (3) systematic reviews and evidence synthesis, (4) social and ethical issues in public health, and (5) governance, policy, and innovation. The review also identified underexplored areas and inconsistencies across studies, highlighting significant research gaps.

Conclusions:

AI has demonstrated substantial potential to enhance public health practice and research. However, further investigation is needed to address ethical, governance, and implementation challenges. Future research should focus on strengthening policy frameworks, improving transparency and accountability in AI systems, and promoting interdisciplinary collaboration to ensure responsible and equitable integration of AI into public health.

Clinicaltrial: