JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming. Public health aims to protect and improve the health of communities and populations at local, regional, national, and global level, but typically suffers from limited resources. AI may lead to improved efficiency or effectiveness of public health interventions, but there needs to be appropriate consideration of the risks of implementation.

Objective:

This scoping review aims to identify the potential roles and risks of AI in public health.

Methods:

We searched Medline, Embase, Web of Science (Core Collection), and Scopus for relevant reports published between 01 January 2020 and 08 September 2025. Two reviewers independently screened reports. Eligible reports were primary studies, reviews, opinion pieces, commentaries, and editorials relevant to high income countries. Data were extracted by one reviewer and checked by a second. We combined substantively similar potential roles and risks by narrative theme across reports. We refined the protocol and gained feedback on initial findings from stakeholder consultation with 9 people from Health Determinants Research Collaborations, and 9 people from data science institutes, public health agencies, and national Local Government networks.

Results:

We screened 1,805 records and 320 full texts, including 67 reports. These reports considered 44 potential roles of AI across eight public health contexts. Potential roles primarily included using machine learning to make predictions, analyse data, and make recommendations, and using large language models to help tailor educational content or communications. Most frequently indicated risks were bias and generalisability, confidentiality and ethical concerns, and exacerbation of inequalities. Specifically, AI could lead to perpetuation or reinforcement of health inequalities and discrimination, a reduction in public trust for public health communications and interventions, and inaccurate predictions, unintended consequences, or ineffective interventions.

Conclusions:

While we did not consider how plausible or beneficial the identified potential roles of AI in public health may ultimately be, when adopting any new intervention, the intervention should be evaluated for anticipated effectiveness (and cost-effectiveness), with careful consideration of the risks. Future research needs to evaluate the effectiveness of AI interventions across the range of public health domains, with a focus on assessing the potential of inequity generating impacts and harms. There needs to be meaningful community engagement and co-production with diverse and underrepresented people to help mitigate against inequity (and improve trust) when adopting AI tools. Stakeholders emphasised the importance of examining how interventions can be optimally diffused and implemented within public health systems to maximise the likelihood of any realising any positive effects. Clinical Trial: Zenodo 15971614