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

Authors

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

Published in

Safety and health at work. Volume 17. Issue 2. Pages 155-171. Epub Feb 24, 2026.

Abstract

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.
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.
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.
The application of AI to OHS requires proactive policy, worker participation, and evidence-informed risk management, with rigorous impact assessments and ongoing research.

PMID:
42382198
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

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