Where is AI actually being used in public health? A review found only eight reports
Researchers screened 5,400 records and 155 full texts but found only eight reports describing AI used in public-health practice relevant to high-income countries outside outbreaks. The evidence was mixed and usually did not test effectiveness.
The short version
- What happened
- The review searched four databases for reports from January 2020 to 24 July 2025, screened 5,400 records and assessed 155 full texts.
- Who it affects
- Public-health decisions affect whole communities, so weakly evaluated tools can scale missed needs, confusing advice or unequal access. Practitioners need service outcomes and failure reporting, not only technical demonstrations.
- What remains unproven
- Scoping review
Keep in mind
Scoping review
Eight included reports
High-income contexts
Outbreaks excluded
Search ended 24 July 2025
Mixed source designs
No pooled effectiveness estimate
Most reports lacked robust evaluation
Source analysis · 1 linked source. Primary analysed source date: 29 October 2026.
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Read the full report · 4 min The evidence, methods, implications and limitations
The scarcity—not a performance score—is the main finding
A peer-reviewed scoping review found a strikingly thin public record of AI being used in public-health practice. The researchers searched MEDLINE, Embase, Web of Science and Scopus for material published from January 2020 to 24 July 2025. Two reviewers independently screened 5,400 records and 155 full texts. Only eight reports met the definition of an AI tool used in practice in a public-health context relevant to high-income countries, outside outbreak response.
Those eight reports covered three chatbot uses, one machine-learning forecast of health-service demand, two symptom-checking tools and two uses of AI in evidence synthesis. The review deliberately required evidence that a tool had reached practice, a pilot or another public-facing use. A laboratory comparison or proposed use without practical exposure did not qualify. That makes the result useful for procurement: a large research literature does not necessarily translate into inspectable operating evidence.[1]
Mixed examples do not establish an average benefit
The included reports did not support one overall effectiveness estimate. One chatbot was described as helpful by users; another was judged ineffective and capable of undermining trust in the organisation behind it. A large-language-model system used with socially isolated people showed benefits and drawbacks. Most reports did not robustly evaluate effectiveness, cost, unequal effects or unintended harm, and the review included commentaries and editorials as well as primary studies.
Public-health teams therefore cannot treat 'AI' as a single intervention. A demand forecast, symptom checker and conversational service create different error paths and need different comparators. Before adoption, teams should specify the population reached, existing workflow, eligible denominator, accuracy or service outcome, staff checking burden, cost, complaints and equity effects. Publishing negative and inconclusive deployments matters because otherwise the next authority sees only vendor demonstrations and successful pilots.[1]
The distinction that matters
How 5,400 records became eight practice reports
The review separated proposed or laboratory uses from tools that had entered public-health practice or a public-facing pilot.
Records
5,400 screened
Broad database search for AI and public-health practice
Full texts
155 assessed
Reports checked against the practice-focused criteria
Included
8 reports
Mixed uses and little robust effectiveness evidence
The review is deliberately narrow
The search excluded outbreaks through a protocol refinement made before searching, and it focused on contexts relevant to high-income countries. That improves relevance to UK practice but limits global generalisation and omits a large emergency-response literature. Data extraction was completed by one reviewer and checked by another. The study was funded by UK public research programmes, and the authors declared no conflicts of interest.
The assessment would change with prospective, independently reported deployments that publish pre-specified outcomes and costs. For now, the defensible conclusion is modest: public-health agencies may be experimenting with AI, but the accessible evidence describing what was used, for whom and with what result remains too sparse to infer routine benefit.[1]
Watch the background, then check the evidence
Illustrative photo · not a video frame
The Royal Society · 44 min 28 sec · Background explainer
Is this the AI future we want? | The Royal Society
YouTube loads only when you choose; playback starts when you press play.
A Royal Society discussion with Madhumita Murgia and biomedical engineer Alison Noble on AI’s potential in healthcare, science and culture, recorded at the 30 June 2026 event.
Evidence check: A discussion of opportunities and risks, including future possibilities. It is not evidence that every application described is available or clinically proven.
Published 2026-07-01. Source checked 14 October 2026. Check captions on the publisher’s video page.
Check the healthcare evidencePhoto: Daniel Sone / National Cancer Institute / Unsplash · Licence
Continue the story
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Read the next reportExplore the full source and evidence record
What this means for people
- Public-health decisions affect whole communities, so weakly evaluated tools can scale missed needs, confusing advice or unequal access. Practitioners need service outcomes and failure reporting, not only technical demonstrations.
Global context
The review was designed for high-income public-health systems and does not represent the evidence or operating conditions in low- and middle-income countries. Workforce capacity, disease burden, digital access and public-health delivery structures differ substantially.
What the evidence does not yet show
- Scoping review
- Eight included reports
- High-income contexts
- Outbreaks excluded
- Search ended 24 July 2025
- Mixed source designs
- No pooled effectiveness estimate
- Most reports lacked robust evaluation
What to watch next
- Prospective public-health deployments
- Pre-registered service outcomes
- Cost-effectiveness
- Equity by population group
- Complaint and harm reporting
- Independent replication
- Accessible negative results
Living evidence record
Impact record IAI-02YPZXU
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Present
Record status
Monitoring
Last checked
30 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what Journal of Medical Internet Research published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 30 October 2026
Research question, topics and regions
How much accessible evidence documents AI being used in routine public-health practice outside outbreaks
Topics: Public health · Evidence gaps · Chatbots · Symptom checking · Demand forecasting · Evidence synthesis
Regions: United Kingdom · Europe · High-income countries
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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