Related Experiment Videos
Ethical Use of Artificial Intelligence in Public Health: Mitigating Risks and Advancing Health Equity
1Terry Adirim is with the Department of Pediatrics and the Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD. Amy Molten is with the Department of Pediatrics, Tufts University School of Medicine, Boston, MA.
American Journal of Public Health
|August 13, 2026
Summary
Artificial intelligence (AI) offers public health tools but lacks ethical guardrails, risking harm to underserved populations. Recommendations include equity assessments and community validation for responsible AI deployment.
Area of Science:
- Public Health
- Health Informatics
- Bioethics
Background:
- Artificial intelligence (AI) integration in public health offers potential for enhanced surveillance, interventions, and management.
- Current AI deployment in public health faces fragmented regulations and insufficient equity safeguards.
- Industry-driven AI development often lacks ethical considerations, posing risks, especially to marginalized communities.
Purpose of the Study:
- To examine AI applications in public health through the lens of ethical principles.
- To identify critical infrastructure and governance gaps in AI implementation across various public health settings.
- To propose strategies for the ethical and equitable use of AI in public health.
Main Methods:
- Analysis of current AI applications in public health.
- Evaluation through established ethical principles, focusing on marginalized communities.
- Identification of governance and infrastructure gaps at federal, state, local, and Tribal levels.
Main Results:
- AI in public health presents significant ethical, operational, and equity risks due to inadequate oversight.
- Existing frameworks lack sufficient guardrails for responsible AI development and deployment.
- Critical infrastructure and governance gaps were identified across public health systems.
Conclusions:
- Mandatory equity impact assessments are crucial for AI in public health.
- Validation in intended communities and adherence to data sovereignty principles are essential.
- Transparency, community engagement, and workforce capacity building are vital for responsible AI adoption.
