Evidence map›Paper›PMID 42382198›Full record

ReviewSafety and health at work2026

Artificial Intelligence and Occupational Health: Global Umbrella Review of Applications and Limitations.

Alexis Descatha, Devan Hawkins, Grace Sembajwe, Saber Aloui, Eric Fadel, Diana Gagliardi, Francesco S Violante, Malcom R Sim, Marc Fadel, Seong-Kyu Kang

Abstract readReview
In one paragraph

Review in Safety and health at work, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Alexis DescathaUniv Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Ester Team, SFR ICAT, CAPTV Prevention, Angers, France.
Devan HawkinsInternational Commission on Occupational Health, Italy.
Grace SembajweIndiana University, School of Public Health, Bloomington, IA, USA.
Saber AlouiUniv Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Ester Team, SFR ICAT, CAPTV Prevention, Angers, France.
Eric FadelUniv Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Ester Team, SFR ICAT, CAPTV Prevention, Angers, France.
Diana GagliardiInternational Commission on Occupational Health, Italy.
Francesco S ViolanteInternational Commission on Occupational Health, Italy.
Malcom R SimInternational Commission on Occupational Health, Italy.
Marc FadelUniv Angers, CHU Angers, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Ester Team, SFR ICAT, CAPTV Prevention, Angers, France.
Seong-Kyu KangInternational Commission on Occupational Health, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial Intelligence (AI) and information processing are technologies that will likely be the transformational for Occupational Health and Safety (OHS). We aim to provide a global umbrella review of the application of AI in occupational health, highlighting its strengths, limitations, and perspectives regarding its application. Methods: In PubMed, Web of Science, and Scopus, we identified reviews published in peer-reviewed journals, as well as reports in the gray literature, dealing with the application of AI in OHS. Data extraction from these publications focused on applications of the technologies, strengths, and limitations of their utilization. Results: From 1,884 initial hits, 33 reviews were included in this review, with only 4 systematic and 12 other systematized reviews. Many diverse AI applications in occupational health were found. Studies were identified from all continents and were mostly published in the last 15 years. The findings suggested that AI might have positive applications (risk prevention and monitoring, diagnosis, health, and well-being, training and skills development, automation and robotics, sector-specific applications, and organizational efficiency). Data and security, reliability, and limitations of AI systems, impacts on workers, governance and ethics, and scientific and methodological limitations were noted. However, the level of evidence is low and further specific studies will be needed, especially worker-centered studies with a health equity perspective. Conclusion: The application of AI to OHS requires proactive policy, worker participation, and evidence-informed risk management, with rigorous impact assessments and ongoing research.

Indexed as

AIDecisionOccupationalOHSReviewWorkWork-related

Identifiers

PMID42382198
PMCPMC13315994

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.