Evidence map›Paper›PMID 39882489›Full record

ArticleBulletin of the World Health Organization2025

Artificial intelligence, recessionary pressures and population health.

Jo-An Occhipinti, Ante Prodan, William Hynes, John Buchanan, Roy Green, Sharan Burrow, Harris A Eyre, Adam Skinner, Ian B Hickie, Mark Heffernan and 3 more

Abstract read
In one paragraph

Article in Bulletin of the World Health Organization, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

13 authors.

Jo-An OcchipintiMental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
Ante ProdanSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.
William HynesWorld Bank, Paris, France.
John BuchananBusiness School, University of Sydney, Sydney, Australia.
Roy GreenUTS Business School, University of Technology Sydney, Sydney, Australia.
Sharan BurrowLondon School of Economics Grantham Institute, London, England.
Harris A EyreBaker Institute for Public Policy, Rice University, Houston, United States of America.
Adam SkinnerMental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
Ian B HickieMental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
Mark HeffernanSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.
Yun Ju Christine SongMental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
Goran UjdurMental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
Marcel TannerSwiss Academies of Arts and Sciences, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Economic and labour policies have a considerable influence on health and well-being through direct financial impacts, and by shaping social and physical environments. Strong economies are important for public health investment and employment, yet the rapid rise of generative artificial intelligence (AI) has the potential to reshape economies, presenting challenges beyond mere temporary market disruption. Generative AI can perform non-routine cognitive tasks, previously unattainable though traditional automation, creating new efficiencies. While this technology offers opportunities for innovation and productivity, its labour-displacing potential raises serious concerns about economic stability and social equity, both of which are critical to health. Job displacement driven by generative AI could worsen income inequality, shrink middle-class opportunities and reduce consumer demand, triggering recessionary pressures. In this article, we propose the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures may emerge, and which market forces alone cannot correct. Traditional responses to such pressures, like fiscal stimulus or monetary easing, may be ineffective in addressing structural disruptions to labour markets caused by generative AI. We call for a proactive global response to harness the benefits of generative AI while mitigating risks. This response should focus on reorienting economic systems towards collective well-being, as emphasized in the World Health Assembly resolution

Indexed as

Artificial IntelligenceEconomic RecessionPopulation HealthGlobal HealthHumans

Identifiers

PMID39882489
PMCPMC11774225

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.