## 4 Discussion  
As indicated above, one of the difficulties of this type of bibliometric analysis of co-words is the correct choice of search terms in such a way that all related publications are identified, but without conditioning the results obtained. In this sense, attention has been paid mainly to the results that were not used as search terms. As already indicated in the Methodology section, the names of specific artificial intelligence techniques (neural networks, fuzzy logic, expert systems, support vector machine, random forest, etc.) were not used in the searches. In this sense, in the results obtained, only some of them appeared as relevant in the clusters, such as neural networks, fuzzy logic (Measurement domain), and Bayesian networks (Uncertainty domain). The importance of this last technique in recent years in the field of project management and uncertainty has already been pointed out by other authors [50].  
Figure 11: Clusters identified in the Uncertainty domain.  
Figure 10: Clusters identified in the Measurement domain.  
One of the novelties of this study is the division of project management into the domains defined by the PMI with the aim of identifying the main research trends in each of them. This separation made it possible to identify areas of interest that are both specific to each domain and common to many of them: for example, clusters related to the success of the project and team members in the Team domain, with customer requirements and competition in the Stakeholders domain, effort estimation in the Planning domain, costs and human resource management in the Work domain, and, as expected, the perception, analysis, and management of risks in the Uncertainty domain. On the contrary, several areas of research frequently appear in different domains.  
Terms related to decision making appear in all PMI domains. In other words, the development of decision-making support tools and systems using artificial intelligence to help in the management of innovation projects is a topic of wide interest. The scientific publications retrieved show that, up to now, these systems have been developed for specific sectors, projects, or areas [51; 52; 53] and not from a full general project-management perspective.  
Numerous specific features are studied in software projects, such as risks [54; 55], performance [56], human resource management [57], quality [58; 59], and effort estimation [60; 61; 62], as well as more general or methodological aspects [63; 64].  
The construction sector is usually linked to information and knowledge management for product development and sustainability. Other recent research [65; 66; 67; 68] focusing on the impact of artificial intelligence in the construction sector reached similar conclusions, with a continued increase in the number of scientific investigations in this field.  
Product-development projects and their sustainability are another recurring research topic that frequently appears linked to knowledge management or decision-support systems.  
The current impact of artificial intelligence in many different fields, including science, is being highlighted in many publications [69]. [PERSON] et al. [70] recently proposed considering artificial intelligence as \"an emerging general method of invention\" and, on this basis, deriving its policy implications.  
As limitations of this study, it is possible to mention the one inherent to this type of bibliographical analysis, based on the frequency of appearance of the terms, which allows for detecting general and continuous trends over time, but which does not allow for revealing the most recent lines of research. Another limitation is the use of a single database (Scopus) due to the difficulty of combining systems with different indexing methods using keywords. The same analysis could be extended to other reference bibliographic databases such as the Web of Science (WOS) [71], which would allow the findings to be compared.