# Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis
## 2 Research Methodology  
To address the aforementioned research questions, we employed the bibliometric analysis technique as our methodological approach. Within this framework, the careful selection of databases for sourcing information, along with the criteria used to determine the eligibility of studies for analysis, was deemed crucial. With regard to the first parameter, our objective was to encompass a broad spectrum of academic publications from databases widely recognized within the scientific community. Consequently, we opted for the Scopus and PubMed databases, renowned for their extensive collection of academic publications pertaining to AI and medicine. However, these two sources are rarely combined for bibliometric analysis due to significant disparities in the information they provide. Specifically, the CSV file obtained from PubMed lacks certain data such as the number of citations, associations, abstracts, and index keywords, which would need to be manually extracted by the researcher from each article's PubMed page. Moreover, the different categorizations used by the two databases at the publication type level do not align. For instance, Scopus classifies documents as \"Conference Paper, Book Chapter, Book, Review, Conference Review\", while the corresponding PubMed categories include \"Clinical Conference, Consensus Development Conference, NIH, Books and Papers, Review, Systematic Review\". This issue was addressed with seriousness to facilitate the continuation of the research process and the effort to integrate the two databases. This was particularly due to our focus during the search on studies published in scientific journals, books, and conference proceedings, while excluding newspapers, opinion pieces, and news articles. Furthermore, from our sample, we deemed it appropriate to exclude all kinds of reviews, aiming primarily to identify the bibliometric characteristics of original articles. The practice of omitting reviews from the bibliometric analysis was also adopted in other studies assessing the role of AI in healthcare, as demonstrated by [PERSON] et al. (2018) in their study on ethics and the use of AI in the sector. The rationale behind excluding review articles was based on their tendency to over-represent certain topics through the aggregation of multiple primary studies, which could skew the results of our thematic analysis. The detailed screening criteria for the exclusion of reviews are presented in Table 1.  
Based on the rationale outlined above, we initially retrieved data from the two databases on 29 March 2024. We used the keywords \"medical\" or \"healthcare\", \"interaction\", and \"artificial intelligence\" in the title, abstract, and keywords (TI-ABS-KEY) field of the Scopus database, identifying a total of 2241 articles. Similarly, in the PubMed database, employing the same terms resulted in 319 articles. Table 2 summarizes the queried databases and the specific search terms used. As an inclusion criterion for both databases, articles needed to be in English. The searches in both databases were followed by comparing the results to identify and eliminate duplicate entries--articles included in both databases. Each potential duplicate was manually checked to confirm its status as a duplicate. This involved verifying the titles, authors, publication years, and other relevant details to ensure accuracy. Consequently, a total of 195 articles found in both Scopus and PubMed were removed from our sample. Within this group, we identified an article by [PERSON] et al. (2018) that was listed twice in the Scopus database, as well as instances where the same article appeared with different publication years in the two databases ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020). After identifying these duplicates, each entry was meticulously reviewed to confirm its uniqueness in terms of content and publication details, ensuring the removal of any erroneous duplicates.