Evidence map›Paper›PMID 40031938›Full record

ReviewThe Lancet. Public health2025

Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions.

Dimitra Panteli, Keyrellous Adib, Stefan Buttigieg, Francisco Goiana-da-Silva, Katharina Ladewig, Natasha Azzopardi-Muscat, Josep Figueras, David Novillo-Ortiz, Martin McKee

Abstract readReview
In one paragraph

Review in The Lancet. Public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

81 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2026
    Review
  5. Article
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

21 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Dimitra PanteliEuropean Observatory on Health Systems and Policies, Brussels, Belgium. Electronic address: pantelid@obs.who.int.
Keyrellous AdibWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Stefan ButtigiegMinistry of Health, St Luke's Hospital Campus, Pieta, Malta.
Francisco Goiana-da-SilvaNOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal.
Katharina LadewigCentre for Artificial Intelligence in Public Health-Research, Robert Koch Institute, Wildau, Germany.
Natasha Azzopardi-MuscatWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Josep FiguerasEuropean Observatory on Health Systems and Policies, Brussels, Belgium.
David Novillo-OrtizWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Martin McKeeEuropean Observatory on Health Systems and Policies, Brussels, Belgium; London School of Hygiene & Tropical Medicine, London, UK.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Artificial intelligence (AI) can rapidly analyse large and complex datasets, extract tailored recommendations, support decision making, and improve the efficiency of many tasks that involve the processing of data, text, or images. As such, AI has the potential to revolutionise public health practice and research, but accompanying challenges need to be addressed. AI can be used to support public health surveillance, epidemiological research, communication, the allocation of resources, and other forms of decision making. It can also improve productivity in daily public health work. Core challenges to its widespread adoption span equity, accountability, data privacy, the need for robust digital infrastructures, and workforce skills. Policy makers must acknowledge that robust regulatory frameworks covering the lifecycle of relevant technologies are needed, alongside sustained investment in infrastructure and workforce development. Public health institutions can play a key part in advancing the meaningful use of AI in public health by ensuring their staff are up to date regarding existing regulatory provisions and ethical principles for the development and use of AI technologies, thinking about how to prioritise equity in AI design and implementation, investing in systems that can securely process the large volumes of data needed for AI applications and in data governance and cybersecurity, promoting the ethical use of AI through clear guidelines that align with human rights and the public good, and considering AI's environmental impact.

Indexed as

Artificial IntelligencePublic HealthAdministrative PersonnelHumans

Identifiers

PMID40031938
PMCPMC12040707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.