Workplace safety remains a critical issue worldwide, with approximately three million workers dying annually each year from work-related accidents and diseases, highlighting the urgent need for more effective prevention measures. Technological advances, particularly in Artificial Intelligence (AI) and Natural Language Processing (NLP), offer innovative approaches to occupational risk prevention in various sectors. This literature review systematically examines the application of AI models, in particular, Large Language Models (LLMs) and NLP, to occupational risk prevention. The review includes studies published between 2013 and 2024, sourced from Web of Science and Google Scholar, using a combination of key terms related to occupational safety and AI technologies. The results show the increasing integration of AI and NLP across multiple industries, including aviation, construction, chemical, and transport sectors, to enhance safety management. Notable applications include real-time risk mapping, automated classification of safety incidents, and predictive modelling of occupational hazards. Thus, this review highlights the potential of AI and NLP technologies to transform occupational risk prevention by providing more accurate, efficient, and predictive safety strategies. However, challenges such as data quality, model transparency, and multilingual support remain. Future research should focus on addressing these limitations and further exploring AI and NLP applications to effectively mitigate workplace hazards.
Martínez et al. (2025) studied this question.