New Trends in the Use of Artificial Intelligence and Natural Language Processing to Occupational Risks Prevention

1. Introduction

Occupational Safety and Health (OSH) is a major concern for all countries worldwide (Zhang et al., 2019), and its management is an ongoing challenge to protect the health and safety of workers and ensure a safe and healthy working environment. The latest International Labor Organization (ILO) estimates that nearly three million workers die each year from work-related accidents and diseases, an increase of more than 5 per cent compared to 2015 (ILO, 2023), denoting an urgent need for more action to prevent work-related accidents and diseases. Although the statistics remain alarming, working conditions have improved tremendously over the years (Badri et al., 2018). Advances in science and technology, such as engineering controls, safer machinery and processes, collective and individual protective equipment, and the implementation of regulations and labor inspections, have significantly reduced the incidence of occupational accidents and diseases associated with industrialization (Kim et al., 2016). In addition, the development of a preventive culture in organizational settings have been crucial in optimizing health and safety management. Industrial development began worldwide in the eighteenth century (Sharma & Singh, 2020) and has been characterized by a series of events that have triggered different revolutions over the years, as shown in Figure 1. These revolutions have been driven by technological transformations that have led to changes in the way industry operate and important social changes (Vinitha et al., 2020; Xu et al., 2018).

The First Industrial Revolution began with the introduction of the power loom in 1784 and was characterized by shifts towards the intensification of work activities. In this period, water power and the steam engine played a decisive role, both in their contributions to industry and transport (Sharma & Singh, 2020). In the 19th century, the Second Industrial Revolution was born with the invention of electricity production, innovations in development, the use of new materials (alloys, synthetic plastics), mass production and assembly lines (Zhang & Yang, 2020). With the appearance of the first programmable logic controller (PLC) in 1969, the transition from the invention and manufacture of analogue to digital electronic devices, automation, and the incorporation of information technologies (ICT) into industrial processes, the Third Industrial Revolution was born (Fonseca, 2018), which encouraged the glocalization of production and the relocation of jobs (Roberts, 2015).

The Fourth Industrial Revolution, or Industry 4.0 (early 21st century), is marked by technological developments with a certain autonomy and self-behavior, mainly focused on industrial automation robotic production through the integration of digital technology, information and communication technologies within an intelligent environment (Leesakul et al., 2022; Milea & Cioca, 2024; Gomez Miranda and Gonçalves, 2024). Specifically, this digital transformation is driven by technologies such as Blockchain, the Internet of Things, Big Data, Cyber-physical systems, Cobotics, Artificial Intelligence (AI), Natural Language Processing (NLP), Cloud Computing, Augmented Reality (AR) and Virtual Reality (VR), which aim to optimize production processes, increasing productivity and efficiency(Milea & Cioca, 2024). Finally, from 2021 onwards, the futuristic Fifth Industrial Revolution emerged, based on humans-robot collaboration to increase creativity and innovation by allowing robots to perform monotonous activities (Miraz et al., 2022).

On the other hand, the evolution in the field of OSH has always followed the different revolutionary advances in the industry (Badri et al., 2018) which has made it possible to react and propose effective solutions to be able to control occupational risks that may manifest themselves in the face of technological advances, innovations, changes in working methods, organization, work teams, processes, products, and workplace itself. Indeed, the nature of a changing work environment brings with it a series of OSH challenges and opportunities, which is why its management is essential to ensure workers’ health, business sustainability and social stability (Wang et al., 2020)

Over the years, in most industrialized and developed countries, reactivity has given way to proactivity (Badri et al., 2018) which promotes a fully preventive approach to OSH that allows action from the source to eliminate risks. This allows the appropriate decisions to be made sufficiently in advance to anticipate possible undesirable events that could harm workers. To this end, the incorporation of digitalization is now beginning to offer new opportunities to innovate, improve and address new and emerging risks in the field of occupational risk prevention, through the incorporation of neurocognitive computing technologies, AI and NLP. Recent studies highlight the role of AI in the risk of occupational disease, analyzing workplace hazards, and enhancing safety measures (Garvin & Kimbleton, 2021; Mollaei et al., 2023; Pishgar et al., 2021; Westhoven, 2022; Yimyam & Ketcham, 2022).

Thus, the evolution of occupational risk prevention has been characterized by a continuous expansion of its focus and methods. Initially focused on occupational health, it has transformed into a comprehensive approach that includes safety and health management and the early use of emerging technologies for the benefit of workers. This shift reflects a deeper understanding of workplace hazards, including chemical risks and psychosocial factors. The development of a preventive culture within organizational settings has been crucial in optimizing safety and health management systems. Recent strategies include dynamic risk assessment which helps organizations become better able to adapt to rapidly changing business or technological dynamics, putting them in a better position to respond to changes in business processes and their associated OSH risks (van Gulijk, 2021).

Today, this evolution is experiencing breakthrough, especially with the integration of AI and NLP. Recent studies highlight the role of AI in assessing the risk of occupational disease risk, analyzing workplace hazards, and enhancing safety measures. (Mollaei et al., 2023; Yimyam & Ketcham, 2022); Pishgar et al., 2021; Westhoven, 2022; Garvin & Kimbleton, 2021) Specifically, NLP is demonstrating great potential in processing and interpreting large data sets for risk analysis. These technologies are central to the development of more efficient, accurate, and predictive occupational risk prevention strategies.

The objective of this study is to conduct a systematic review of the application of Artificial Intelligence (AI) models, particularly Large Language Models (LLMs) and Natural Language Processing (NLP), within the domain of occupational risk prevention. This review aims to elucidate how these advanced technologies are being employed across various industrial sectors, including aviation, construction, the chemical industry, and transportation, to improve workplace safety and risk management. Specifically, the study will identify key technological applications such as real-time risk mapping, automated safety incident classification, and predictive modeling of occupational hazards. Furthermore, it seeks to address current challenges related to data quality, model transparency, and multilingual support while providing insights for future research to overcome these limitations and advance the efficacy of AI-driven occupational risk prevention strategies.

2. Methodology

This literature review explored the application of AI models, specifically LLM and NLP, in different areas of occupational risk prevention. The review focused on relevant studies published between 2013 and 2024, using a systematic search strategy across multiple databases and considering specific inclusion criteria.

2.1. Data Sources and Search Strategy

The primary sources for this review were the Web of Science and Google Scholar databases. The search was carried out using specific combinations of key terms: “occupational risk prevention” AND “artificial intelligence”, “occupational risk prevention” AND “Large Language Models”, AND “occupational risk prevention” AND “Natural Language Processing”. In addition to direct retrieval of articles, a snowballing technique was employed to review the citations within these works and the articles that cited them. This approach ensured comprehensive coverage of the existing literature relevant to the research topic.

2.2. Inclusion Criteria

To be included in this review, studies had to meet several criteria:

• Publication venue: Only articles published in peer-reviewed scientific journals indexed in the Journal Citation Reports (JCR) were considered.
• Publication period: Studies had to be published between 2013 and 2024.
• Content relevance: The studies needed to contain the specified key terms either in the title, abstract, or keywords or deal with the application of specific methods based on AI, LLM, and NLP in the context of occupational risk prevention.
• Methodological focus: Each study was manually reviewed to ensure that it specifically applied LLM and NLP methodologies to the field of occupational risk prevention.

2.3. Screening and Selection Process

The initial search yielded a large number of potential articles. Each article was subjected to a multi-step screening process. Firstly, the titles and abstracts were reviewed to identify studies that appeared to meet the inclusion criteria. Articles passing this stage underwent a full-text review to confirm the relevance and ensure that the research applied specific LLM and NLP methodologies in the context of occupational risk prevention. Studies were excluded if they did not directly address the application of these technologies in this specific area or if they were not empirical research articles or reviews published in peer-reviewed journals.

2.4. Data Analysis and Synthesis

The included studies were categorized according to of application in areas across different industrial sectors, reflecting the diverse contexts in which LLM and NLP technologies have been used for occupational risk prevention in recent years. Data extracted from the studies included the type of AI technology used, the specific methods and models employed, the industrial context, the objectives of the application, and the reported outcomes or findings. This structured approach facilitated the identification of trends, gaps, and emerging themes in the use of AI for occupational risk prevention.

This methodology ensured a comprehensive and systematic review of the literature, focusing on the application of LLM and NLP models in occupational risk prevention. By adhering to strict inclusion criteria and employing a rigorous screening process, the review provides a robust foundation for understanding how these advanced AI methodologies are being applied across various industrial sectors to enhance workplace safety and risk management. The selected works were those that met the inclusion criteria, as shown in Figure 2.

3. Results

As pointed out by Zhao et al (2018), the benefits of using NLP methods in occupational risks prevention are multiple; for example, these approaches allow valuable information to be extracted and processed from large amounts of data. Future research directions include pattern recognition, in-situ identification of actual events, and fully automated methods (Zhao et al., 2018). More specifically, previous reviews have highlighted the high potential of AI, LLMs and NLP methods in different areas of occupational risk prevention, as summarized in Table 1, such as exploring the impact of NLP applications in the field of aviation safety (Yang & Huang, 2023; Kierszbaum & Lapasset, 2020), and in other safety-critical industries such as transport, medical and construction (Ricketts et al., 2023), as well as for occupational injury analysis (Khairuddin et al., 2022), unveiling the influential aspects of this field through descriptive and scient metric analyses (Sarkar & Maiti, 2020).

LLM and AI are demonstrating great potential for development in different areas of occupational risk prevention, in sectors such as aviation and construction, even in specific risks such as Fall from Height (FFH), and in the chemical industry, as well as in the transport system, including railway, in the nuclear power generation sector, and for the protection of mine workers and to avoid medical errors.

Among the advantages that we can find in the use of methodologies based on AI, LLM and NLP in the field of occupational risk prevention, some are particularly interesting due to their high general applicability to multiple sectors, such as the generation of risk maps in real-time. For example, the application of dynamic real-time analysis using multimodal data fusion to enhance occupational risks prevention through the development of risk maps for workplaces, using machine/deep learning techniques by analysing data from diverse sources such as images, videos, documents, mobile applications and sensors/IoT. Thus, the combination of computer vision, NLP techniques, and sensor data analysis enables automated root cause identification, damage prevention, and disaster recovery, dynamically updating risk assessments in real-time. (Dalal & Bassu, 2020).

It is also worth mentioning that an important part of the success of the application of LLM and NLP-based methods lies in their ability to extract and analyze information in an automated way from large datasets contained in reports (e.g., accident reports), where the information can be structured to address a variety of problems, such as the limitations of generic and static checklists, which often do not apply to specific workplace contexts (Westhoven & Jadid, 2023), or mor interestingly, the information may not have been previously structured.

Thus, in relation to the use of unstructured information, recent research highlights innovative integrations of AI, specifically through NLP and Machine Learning (ML), to refine safety and risk assessments. For example, Kamil, Taleb-Berrouane et al. (2023) combine a variety of NLP and text mining techniques with fuzzy set theory to transform unstructured accident reports into useful data, a methodology that contrasts with others used by Hou et al. (2022), who rationalize incident classification using NLP techniques for text vectorization. On the other hand, Paraskevopoulos et al. (2022) extend the functionality of AI in safety management by introducing a multimodal architecture that synergizes textual and visual data, distinguishing it from other studies primarily focused on text. In addition, Zhao et al. (2020) and Macedo et al. (2023) extend text analysis in different ways, as Zhao focuses on summarizing accident reports, while Macedo aims to correct inaccuracies in report. Furthermore, Baker et al. (2020a) and G. Liu et al. (2021) both refine data extraction and prediction methods, but differ in their approaches, as Baker emphasizes predictive modelling for safety outcomes, while Liu explores causal relationships using clustering techniques. Similarly, Dorsey et al. (2020) and Ekramipooya et al. (2023) aim to improve data quality and analysis efficiency through the use of NLP and AI methods.

Next, a perspective is presented on the impact of methodologies based on AI, LLM and NLP on the advancement of occupational risk prevention in different industrial sectors is presented.

3.1. Aviation

In the field of aviation safety, Miyamoto et al. (2022) and Dong et al. (2021) both used NLP techniques to analyze aviation safety reports. Miyamoto et al. focused on categorizing the causes of flight delay using clustering techniques, revealing maintenance issues as the primary cause. In contrast, Dong et al. combined NLP with deep learning models to automate the identification of primary factors in incident reports, demonstrating superior performance over traditional methods but limiting their scope to the most frequent incident categories. Moreover, Jiao et al. (2022) introduced a novel classification scheme using the XGBoost classifier and OC-POS vectorization to identify risk factors from Chinese aviation reports, indicating great potential for broader applications. Similarly, Kierszbaum et al. (2022) developed a compact, domain-specific language model, demonstrating that specialized pre-training can effectively address the scarcity of domain-specific data in aviation safety NLU tasks, highlighting a trend towards creating more specialized NLP and AI tools tailored to specific data challenges in aviation safety. In addition, Madeira et al. (2021) investigated human factors in aviation incidents, using a hybrid approach of semi-supervised and supervised learning to tackle the challenge of limited labelled data sets, a common issue in AI applications in safety analysis. This study aligns with the work of Rose et al. (2020), who also used NLP and clustering to categorize and visualize safety narratives, but with a focus on integrating numerical and text-based data to enhance accident investigation processes. These studies highlight a significant trend towards using advanced AI and NLP methods to dissect and understand large volumes of aviation safety data, as it is shown in Table 2.

3.2. Construction

Additionally, occupational risk prevention (ORP) in the construction industry has made significant advances through the integration of AI and NLP, as resumed in Table 3 and commented on from now on. For example, Shen et al. (2022) proposed a novel integration of Building Information Modeling (BIM) with an ontology-based safety rule library and NLP, creating a dynamic safety rule-checking system for construction sites and automating the identification of safety risks. In addition, Baker et al. (2020b) used advanced deep learning techniques, including Convolutional Neural Networks and Hierarchical Attention Networks, to analyze construction accident reports, enabling visual interpretation of model predictions to identify injury precursors. Also, Thompson et al. (2020) introduced a Named Entity Recognition scheme tailored to construction safety documents, which aims to structure free-text data into safety strategies. On the contrary, Xu et al. (2021) applied a text mining approach using an information entropy weighted term frequency metric to efficiently extract safety risk factors from metro construction reports, providing a novel quantitative tool for safety risk assessment through the effective analysis of extensive text data.

Furthermore, Fang et al. (2020) and Gadekar & Bugalia (2023) improved text classification in construction safety reports, focusing on the use of Bidirectional Transformers (BERT) for deep learning-based text classification and innovating with a semi-supervised model, respectively, achieving high accuracy with reduced dependence on pre-labelled data. In addition, F. Zhang et al. (2019) and Wang et al. (2021) addressed the classification and categorization of construction accident causes and safety risks, respectively, using different ensemble and text mining techniques to improve the precision of safety data analysis, while Tian et al. (2023) and Fan & Li (2013) allowed the effective information retrieval and analysis from construction documents, employing different AI techniques to optimize the extraction and utilization of safety-related data. Finally, Cheng et al. (2020) combined advanced NLP preprocessing with novel AI methods to enhance the accuracy and effectiveness of construction safety analyses from accident reports. Taken together, these studies highlight a significant shift towards automated, precise, and effective methods of risk identification and safety management in construction through the advanced integration of AI and NLP tools. In relation to the Fall From Height (FFH) risk, Ben Abbes et al. (2022) using NLP to analyze and extract crucial information from the DBkWik database, sourced from 40,000 wikis, by means of knowledge graphs (KGs), highlighting their advantages over deep learning methods, particularly in addressing the limitations of the latter.

3.3. Chemical Industry

The integration of AI and NLP in chemical industry safety has the potential to enhance occupational and environmental safety (see Table 4). Thus, Kamil, Khan et al. (2023) used NLP, Interpretive Structural Model (ISM), and probabilistic techniques to predict and analyze fire and explosion risks, leveraging accident databases for predictive accuracy in safety management practices. On the other hand, Kabir et al. (2023) improved the accuracy of flare system failure analyses in the oil and gas industry by integrating traditional Fault Tree Analysis (FTA) with Dynamic Bayesian Networks (DBNs). In contrast, Kumari et al. (2022) advanced incident prediction by means of Artificial Neural Networks (ANNs) for cause and sub-cause analysis, surpassing traditional models to offer causation clarity. Moreover, B. Wang & Zhao (2022) introduced a novel deep learning framework combining BERT, BiLSTM-CRF, and CNN models to automate the extraction and classification of risk factors from accident reports in confined spaces, addressing the manual labor-intensive and subjective traditional analysis. Additionally, Xu et al. (2022) and Jing et al. (2022) utilized deep learning for analyzing accident causes, applying a CNN model to classify causes and deploying a combination of LSTM and attention mechanisms to enhance text classification of chemical accidents, respectively.

Furthermore, X. Luo et al. (2023) explored the use of NLP to automate the analysis of chemical accidents, categorizing risk factors to support decision making in risk analysis. Also, Macêdo et al. (2022) used BERT models for text mining to enhance quantitative risk analysis in oil refineries. Lastly, Song and Suh (2019) innovated in the detection of anomalies in accident reports by applying a text mining-based method to examine the narratives of accident reports.

3.4. Transport System

The application of NLP and AI has the potential to enhance the accuracy and efficiency of risk assessment and safety management in Transport Systems (see Table 5). Thus, Hughes et al. (2019) used an AI-based model to extract and categorize terms from multilingual incident reports through the application of NLP techniques, achieving a high accuracy rate in categorizing safety incidents in public transport. Also, Valcamonico et al. (2022) also enhanced road safety analysis by integrating Hierarchical Dirichlet Processes and Doc2Vec with machine learning classifiers, showing how combined models can better balance accuracy and explainability in automated report classification. Moreover, Jidkov et al. (2020) focused on maritime risk assessment, employing deep learning and various NLP techniques to capture, process, and analyze data related to maritime safety events such as piracy, hijackings, and smuggling, improving incident classification and information extraction. Meanwhile, Wang & Yin (2020) employed text mining and automatic association rules such as the FP-Growth algorithm to uncover key risk factors in China’s transport sector, providing insights into systemic issues affecting safety. Additionally, Zhang et al. (2021) introduced the use of NLP and deep learning to analyze aviation accident reports with predictive purposes and safety management in aviation. More recently, Ricketts et al. (2022) proposed the use of NLP, rule-based phrase matching and a trained NER model to enhance hazard identification in HAZOP studies of aircraft subsystems, approaching the continuous model refinement and more efficient safety actions.

Specifically, in relation to railway safety and risk prevention, NLP and AI techniques have recently been used to innovate in incident prediction and management. For example, Hughes et al. (2018) developed a semi-automated classification system for close call reports in the GB railway industry, using NLP to associate incident reports with bow-tie accident causation models, with practical applications in categorizing a vast array of unstructured safety-related text. In contrast, Figueres-Esteban et al. (2016) used visual text analysis to extract safety information from the GB railways’ Close Call System, highlighting its potential to identify risks despite the linguistic variation different reporter groups. Also, Wu et al. (2020) introduced NLP methods to improve subway accident decision-making processes in metro accidents with high precision in retrieving relevant past cases and advancing automated accident response systems. Moreover, Heidarysafa et al. (2018) applied deep learning to enhance the accuracy of accident labelling in the US railway sector and advanced the automatic classification of accident causes from narrative texts. Also, Ebrahimi et al. (2023) used NLP and Random Forest to develop a machine learning model capable of predicting evacuation needs following hazardous materials incidents on railways, mapping causal evacuation factors to improve emergency management. Furthermore, Hua et al. (2019) and Liu & Yang (2022) used text mining to improve risk identification in railway safety, extracting accident risk factors from Chinese railway accident reports through convolutional neural networks, and using deep learning techniques to quantify risk relationships in British railway incidents, respectively.

3.5. Other Sectors

As shown in Table 6, other sectors, such as nuclear energy, mining and the prevention of medical errors, also benefit from the integration of AI-based methods, particularly LLM and NLP. In relation to the application of NLP techniques to the nuclear power generation sector, Zhao et al. (2019; 2018) advanced the field by integrating NLP and multimodal data fusion to automatically identify causal relationships in event reports. They used arule-based expert system, the Causal Relationship Identification (CaRI), to effectively capture causal associations with a success rate of 86%. On the other hand, Dalal & Bassu (2020) explored the development of “risk maps” by applying machine learning models to analyze data from sensors and computer vision systems to achieve a dynamic real-time capability to identify risks and prevent workplace accidents. The combination of NLP and AI methods in the field of occupational risk prevention has also recently led to several studies related to mine safety. Thus, Ganguli et al. (2021) carried out automatic data analysis from Mine Health and Safety Management Systems (HSMS) using NLP and Machine Learning (ML), specifically through the development of nine Random Forest (RF) models, demonstrating high accuracy and improved incident categorization.

In contrast, Shekhar and Agarwal (2021) applied text mining of fatality reports to enhance safety in Indian mines, identifying trends and patterns and highlighting the most vulnerable worker demographics and high-risk times periods. Furthermore, Qiu et al. (2021) combined text mining with complex network analysis to identify and quantify factors contributing to coal mine accidents, revealing complex interaction mechanisms and critical causal links, and providing a detailed map of accident causation pathways.

In the context of preventable medical errors, Cohan et al. (2017) proposed the use of NLP techniques to improve the identification and categorization of harmful events in patient care narratives, enhancing patients’ safety. For example, the complexity of clinicians’ reports represents a significant challenge, for which the authors used convolutional and recurrent neural networks, coupled with an attention mechanism, to provide an effective strategy for analyze the textual data to more accurately identify harmful events, categorizing their severity, and outperform existing approaches in detecting these events in large datasets of patient reports. Additionally, Denecke (2016) used NLP methods in the analysis of critical incident reports to address the underutilization of these reports in healthcare to enhance patient safety and quality of care, solving the difficulty and time-consuming nature of retrieving and analyzing these reports manually.

4. Conclusions

This review highlights the transformative potential of AI, particularly Large Language Models LLMs and NLP, in enhancing occupational risk prevention across various industrial sectors. The results show that AI and NLP methods are increasingly being integrated into occupational safety and health (OSH) practices, providing innovative solutions to longstanding challenges in workplace safety management. These technologies have demonstrated significant benefits in terms of automating the identification and classification of safety risks and risk factors, improving real-time hazard detection, and predicting occupational accidents, thereby enabling more proactive and effective risk management strategies.

In the aviation sector, AI and NLP have been successfully applied to analyze large volumes of safety reports, enhancing the detection of risk factors and support decision-making processes. These methods have enabled the identification of complex causal relationships and trends in aviation safety data that were previously difficult to identify using traditional approaches. Similarly, in the construction industry, AI-driven models have been employed to automate the extraction and classification of safety risks from unstructured textual data, such as accident reports, facilitating timely intervention and mitigation potential hazards. The chemical sector has benefited from the integration of AI and NLP to refine hazard analysis, enabling accurate prediction and prevention of accidents through the use of machine learning models and text-mining techniques.

However, several challenges and limitations remain that need to be addressed in order to fully realize the potential of these technologies in occupational risk prevention. Issues such as data quality, availability, and standardization, particularly in industries where safety data is unstructured or multilingual, remain significant barriers. Additionally, the transparency and interpretability of AI models, especially those involving complex machine learning algorithms, are critical concerns that require further attention to ensure trust and acceptance among OSH practitioners. Furthermore, the scarcity of domain-specific datasets for training AI models poses a challenge to achieving the desired levels of accuracy and reliability in real-world applications.

Despite these challenges, the review highlights the promising role of AI and NLP in advancing occupational risk prevention practices. The ability of these technologies to process large and complex datasets, identify patterns, and predict potential risks before they occur offers a significant advantage in promoting safer working environments. Future research should focus on developing more sophisticated AI models that can handle different types of data and address the limitations of current methods, such as enhancing model transparency and ensuring compatibility across different languages and contexts.

In conclusion, AI, LLMs, and NLP offer significant opportunities to revolutionize occupational risk prevention. By overcoming existing challenges and optimizing these technologies for specific industrial applications, organizations can achieve more efficient, accurate, and proactive approaches to managing workplace hazards, ultimately contributing to improved worker safety and health outcomes worldwide. Continued research and development in this area is essential to fully realise the benefits of AI-driven methodologies in the evolving occupational risk management landscape.