Occupational Health and Safety in the Age of AI: Challenges and Innovations in Risk Management

Abstract

Occupational risk assessment is central to ensuring workplace safety, particularly amid the rapid technological and organizational changes characterizing contemporary work environments. This narrative review aims to examine and synthesize recent advancements in risk assessment models within occupational health and safety (OHS) frameworks. A comprehensive literature search was conducted across databases including Scopus, PubMed, Web of Science, and Google Scholar, focusing on studies published over the past decade. Inclusion criteria centered on empirical studies assessing the effectiveness, integration, and limitations of OHS risk models across high-risk sectors. The results reveal four key themes: the incorporation of artificial intelligence (AI) and Internet of Things (IoT) in predictive safety systems; the efficacy of Safety Management Systems (SMS) in structuring participatory and proactive safety cultures; the effectiveness of dynamic, real-time assessment frameworks in high-risk industries; and the influence of cultural, economic, and policy contexts on model adoption. These findings highlight a notable shift toward responsive, data-driven, and employee-centered safety models. However, systemic barriers such as poor regulatory enforcement, limited awareness, and static theoretical frameworks hinder optimal implementation. The review advocates for policy reforms, employee empowerment strategies, and adaptive frameworks that reflect technological advancements and context-specific needs. These interventions are essential to overcoming identified barriers and improving safety outcomes globally. Future research must aim to bridge theoretical gaps and enhance the generalizability of risk assessment frameworks across diverse workplace environments.

Ähnliche Arbeiten