## 1 Introduction
The most recent bibliographies, analyzed in the second part of this study, consist of works published in reputable scientific journals, and the number of citations for these articles is very high. However, none of the authors use the scientific tool of research questions in their reviews in the form of questions and answers. Taking the above into account, as well as the literature gaps, the following research questions were formulated, which are attempted to be answered in this article:  
1. Which of the four techniques has been used the most by researchers, for what reason, and how are the articles distributed? 2. Into which subcategories can the four techniques be divided, and what problems do they solve in risk assessment? 3. What application fields do the four techniques address in risk analysis?4. Which countries are most involved with the four techniques in relation to their workforce? 5. What are the topics of the top five most published articles, and why were they chosen by other researchers? 6. What is the future trend? The results analyzed in the Section 5 (Research Questions) provide an assessment of the current state and trends on the topic. Additionally, they assist in understanding and interpreting the subject matter, thereby helping to focus the research data. There are several articles that approach the subject from different perspectives or by using only one technique [7; 8; 9; 10; 11; 12]. Specifically, they highlight problems and methods of solving them in a particular sector using a specific technique, such as safety in transportation or issues in the industrial sector, among others. Moreover, they often analyze only one technique and rarely mention a second one. Additionally, the progress of technology and the rapid rate of development in these intelligence techniques do not allow for complacency and necessitate continuous evolution, with the ultimate goal of optimizing safety-related issues. Each of these technologies offers unique strengths, and their integration and collaboration can solve complex problems that are often non-linear and dynamic, where traditional algorithms fail to provide clear solutions [7]. At this point, it is deemed appropriate to specifically reference risk management in the workplace. Risk management and workplace safety involve a set of systematic practices aiming to identify, evaluate, and manage risks in the workplace in order to ensure the minimization of accidents as much as possible. The primary goal is to decrease the probability and severity of injuries and accidents in the workplace. This is achieved by identifying the risk, which may be physical or caused by human intervention [13]. Then, the risk is assessed and the severity of the damage it causes is prioritized. Appropriate measures must then be implemented to mitigate or eliminate the risk. Risk management is an ongoing process, so regular monitoring and reviewing of both the measures and methods used are essential to ensure the effectiveness of workplace safety. The contribution that ChatGPT will make in the coming years is worth noting since it has been influencing and changing the way we think about workplace safety. According to [PERSON] and [PERSON] (2024), artificial intelligence is categorized into three main forms. Artificial Narrow Intelligence (ANI) specializes in specific tasks, such as language translation or weather prediction. Artificial General Intelligence (AGI) mirrors human intelligence, enabling machines to handle various tasks autonomously. Natural Language Processing (NLP) renders machines capable of comprehending and generating human language. NLP facilitates tasks such as sentiment analysis, machine translation, and text generation, making human-computer interactions more intuitive. These models can create safety guidelines, evaluate employee feedback, and generate safety updates tailored to specific construction scenarios [14]. It would be prudent not to rely exclusively on computational intelligence.