Beyond Algorithms: Building Equitable and Ethical AI in Public Health

Artificial intelligence (AI) is rapidly becoming a core competency in the field of public health, complementing surveillance, triage, diagnosis, and operational logistics with the recognition of complex patterns of data that are beyond manual workflow capabilities.But such same strength raises potential threats related to false outputs, bias in automation, inequities, and loss of trust, for which reason comes the appeal of the World Health Organization (WHO) for ethical protection rooted in values of autonomy, transparency, accountability, and inclusiveness.This review brings together existing research on the application of AI in developing public health endeavors, including early warning systems, predictive modeling, clinical diagnostic workflows, and communication plans.Focus is given to the value of validating technologies in actual-world environments and following regulatory advice, including guidelines like Good Machine Learning Practice (GMLP).At the same time, this paper emphasizes the fact that technical tools like AI-powered voice bots or algorithmic tracking must also be coupled with community engagement and open-governance approaches.Lacking in such a social element, even the most advanced technologies threaten to undermine their legitimacy, worsen disinformation, and deepen social division.Key case examples document successful deployments-such as AI-assisted outbreak identification and risk models-as well as proof of concepts still in experimental phases.For the future, scalable impact requires federated and privacy-aware learning, full lifecycle quality systems for detecting drift and bias, and conformance to national digital health plans that embed workforce preparation, governance, and funding modalities.Together, these pieces constitute AI not just as a stand-alone technology but a socio-technical endeavor, whose potential to improve preparedness and response depends on equitable investments in evidence, equity, and trust.