ReviewInternational journal of environmental research and public health2025
Exploring Human-AI Dynamics in Enhancing Workplace Health and Safety: A Narrative Review.
Review in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Artificial Intelligence and Machine Learning for Occupational Risk Prevention: A Systematic Review on Predictive Risk Modeling and Prevention Strategies.Sensors (Basel, Switzerland) · 2025Pooled it
- Artificial Intelligence and Occupational Health: Global Umbrella Review of Applications and Limitations.Safety and health at work · 2026Review
- Analyzing Occupational Safety Managers' and Representatives' Assessments of Collaboration With Occupational Health Care.Workplace health & safety · 2026Article
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
- Enhancing workplace productivity with secure AI using federated contrastive learning model for performance optimization.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is revolutionizing occupational health and safety (OHS) by addressing workplace hazards and enhancing employee well-being. This review explores the broader context of increasing automation and digitalization, focusing on the role of human-AI interaction in improving workplace health, safety, and productivity while considering associated challenges.
methodsA narrative review methodology was employed, involving a comprehensive literature search in PubMed, Embase, and Scopus for studies published within the last 25 years. After screening for relevance and eligibility, a total of 52 articles were included in the final analysis. These publications examined various AI applications in OHS, such as wearable technologies, predictive analytics, and ergonomic tools, with a focus on their contributions and limitations.
resultsKey findings demonstrate that AI enhances hazard detection, enables real-time monitoring, and improves training through immersive simulations, significantly contributing to safer and more efficient workplaces. However, challenges such as data privacy concerns, algorithmic biases, and reduced worker autonomy were identified as significant barriers to broader AI adoption in OHS.
conclusionsAI holds great promise in transforming OHS practices, but its integration requires ethical frameworks and human-centric collaboration models to ensure transparency, equity, and worker empowerment. Addressing these challenges will allow workplaces to harness the full potential of AI in creating safer, healthier, and more sustainable environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.