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. While AI may lead to improved efficiency or effectiveness of public health interventions, adoption of AI interventions should be informed by evidence of effective use in practice.

Objective:

This review aims to summarise how AI-assisted interventions are currently used by public health professionals.

Methods:

We conducted a scoping review to identify the current uses of AI in public health. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core Collection), and Scopus for relevant reports published between 01 January 2020 and 24 July 2025. Two reviewers independently screened reports. Eligible reports were primary studies (experimental or observational), commentaries, and editorials that discussed how AI tools have been used in practice in public health contexts relevant to high income countries. Data were extracted by one reviewer and checked by a second.

Results:

We screened 5,400 records and 155 full texts, including 8 reports. These reports discussed a variety of uses of AI tools across public health domains: three reports used chatbots in different settings, one report used machine learning to forecast health service demand, two reports used AI for symptom checking, and two reports used AI for evidence synthesis. While assessing the effectiveness of AI tools for public health was beyond the scope of this review, and most reports did not robustly evaluate effectiveness, the evidence for effectiveness of AI tools was nonetheless mixed. One report described a chatbot that users found helpful, while another found a specific chatbot to be ineffective, highlighting its potential to undermine trust in the organisation that developed it. Another report highlighted both benefits and drawbacks of a large language model used to communicate with socially isolated individuals. The main finding of this review was that even with a comprehensive search across multiple databases, there were few reports of how AI tools are being used in practice in public health contexts.

Conclusions:

The lack of evidence from public health practice makes it difficult to know which uses of AI, if any, would be useful to adopt into practice. In the absence of evidence, new interventions, including those using AI, should be evaluated to ensure they are effective (and cost-effective). These evaluations should be widely and accessibly disseminated to create an evidence base that other public health teams could use to inform their own decisions around AI tools. Clinical Trial: Zenodo 15971109