Artificial intelligence (AI) holds profound potential to reshape public health. Although its application accelerates threat detection and targeted interventions, the rapid deployment has outpaced the regulatory, validation, and equity safeguards standard to traditional health interventions.
In a recent analytic essay, Dr. Terry Adirim from the Department of Pediatrics and the Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, and Dr. Amy Molten, MD, from the Department of Pediatrics, Tufts University School of Medicine, Boston, MA, examined the ethical challenges of AI use in public health, highlighting the potential for these systems to reinforce existing health inequities. The authors contend that responsible AI deployment requires governance addressing the needs of historically marginalized populations. The study was published online in the American Journal of Public Health on September 9, 2026.
"The most fundamental problem with AI in public health work is structural rather than technical," says Dr. Adirim.
The analysis identifies critical ethical risks like discrimination, surveillance, and privacy violations, across historically marginalized populations, including children, minoritized communities, Indigenous peoples, and people with disabilities.
While analyzing diverse datasets, AI can produce biased outputs. Furthermore, using commercial data like location tracking blurs the line between public health surveillance and consumer privacy, while behavioral AI tools risk manipulation, misinformation, and compromised autonomy.
To mitigate these threats, the analysis recommends equity impact assessments, continuous bias monitoring, data sovereignty protection, and local community validation. It also suggests human oversight in major decisions and national standards for AI transparency and equity testing.
Explaining the value of this approach, Dr. Adirim says, "The threats to children's developmental privacy differ from the data sovereignty concerns of Indigenous communities; the misclassification risks for older adults differ from the surveillance-related fears of undocumented families or incarcerated people; the digital divide barriers facing rural and global populations differ from the engagement-based manipulation risks faced by people with behavioral health conditions. Responsible use of AI in public health practice must therefore be calibrated to this diversity and combine cross-cutting governance with population-specific validation, community engagement, and safeguards tailored to each group."
The authors conclude that responsible public health AI requires governance and accountability to safeguard diverse populations. This approach strengthens public health while protecting equity, transparency, and community trust.
