## 2 Materials and Methods
Analysis: The third stage involved using the bibliometric software Biblioshiny from RStudio's Bibliometrix package. The data extracted from WoS was downloaded in BibTeX format, and the cleaning stage was verified in RStudio v2023.09.1+494.pro2, Bibliometrix R (version 4.3.2). No additional exclusions were necessary at this stage. Step 4. Visualization: In the visualization stage, graphical methods were used to prepare the analysis of sources, authors, and the literature. Step 5. Interpretation: In the final stage, we utilize the graphical representations from step 4 and describe and interpret the results. Table 1 summarizes the queries and steps we conducted to build the database. We utilized the WoS [33] platform to retrieve papers related to ABM and AI. The analysis unfolds in several stages, emphasizing a meticulous process for identifying pertinent works. Titles, abstracts, and keywords are employed to encompass diverse aspects of each paper. The initial step defines the keywords identified in the titles. Table 1 delineates the methodological steps taken in the data selection process during the exploration phase on the WoS. This systematic approach aims to refine the dataset progressively, ensuring relevance to our study on applications of ABM and AI. * Step 1. Title exploration: This stage focuses on identifying articles with titles relevant to ABM and AI, providing an initial delimitation of the dataset. Queries #1 and #2 focus on titles containing keywords related to ABM and AI, respectively. Following these queries, 5135 articles with terms specific to ABM in the title and 211,005 articles with terms specific to AI in the title were identified. Query #3 combines these aspects, refining the search. * Step 2. Abstract exploration: This stage adds relevance by identifying articles with key terms in the abstract and amplifying details about their content. Queries #4 and #5 target abstracts with specific keywords. A total of 11,173 articles with terms specific to ABM in the abstract were obtained, along with a substantial number of 470,238 articles with terms specific to AI in the abstract. Query #6 combines ABM and AI criteria from the abstracts. * Step 3. Keyword exploration: Identifying keyword-based articles consolidates the selection of papers that specifically address the key concepts of the research. Queries #7 and #8 concentrate on keywords associated with ABM and AI. Query #9 combines these keyword criteria. This query focuses on articles that contain both ABM and AI-specific keywords in the keywords section. A total of 134 articles were identified based on this query, providing insights into publications that simultaneously address both ABM and AI in their keyword content. This step further refines the dataset, capturing articles that explicitly mention both key aspects in their keywords. * Step 4. Title/Abstract/Keywords exploration: This stage combines relevant selection criteria to obtain a narrower and more focused subset of articles. Query #10 consolidates the AI-specific keywords from previous queries. A total of 344 articles with specific terms related to AI were identified, consolidating the criteria from previous steps. * Step 5. Language restriction: By limiting it to the English language, we ensure that the included articles are accessible and easily comparable, having a broader international circulation. Query #11 limits results to English publications from the refined set. The results were narrowed down to 340 articles written in English. * Step 6. Document type restriction: This restriction ensures that the analysis focuses on articles, excluding other types of documents that may contain irrelevant information for this study's purpose. Query #12 narrows down the dataset to articles. A total of 226 articles were selected to be consistent with the scope of our study. * Step 7. Year published restriction: This stage ensures a focus on articles published during the relevant period for this study, preemptively eliminating materials that could negatively influence the results.