Comprehensive Global Analysis of Future Trends in Artificial Intelligence-Assisted Veterinary Medicine

Abstract

Background This study conducts a bibliometric analysis of global trends in 'artificial intelligence studies in veterinary medicine'. The analysis aims to summarise the publications of researchers from various disciplines related to artificial intelligence in veterinary medicine, thereby predicting future trends of AI in this field. The primary objective of the study is to investigate publications pertaining to artificial intelligence in veterinary medicine worldwide and to analyse trends and future developments in this area. Methods This bibliometric study examines artificial intelligence research in veterinary medicine conducted worldwide from 1990 to 2024. To achieve this, a search using the keywords 'artificial intelligence' and 'veterinary medicine' was performed in the Web of Science (WOS) database, resulting in the identification of 1497 studies. After excluding irrelevant publications and those outside the scope of articles, a total of 1400 articles were included in the analysis. The data collection process utilised titles, author names, publication years, journal names, and citation counts. All textual data were analysed using VOSviewer software to ensure accuracy and reliability. In this study, analyses conducted through text mining and data visualisation techniques (e.g., bubble maps) facilitated a clearer understanding of the results. Results This study presents information about 1400 articles obtained from the WOS database and a total of 44,700 citations for these articles. The average number of citations per article is 32, with an H-index of 74. A rapid increase in both the number of articles and citations has been observed since 2019. The majority of the articles (30%) were published in the fields of veterinary sciences, artificial intelligence, and computer sciences. The United States, Taiwan and the United Kingdom are the leading countries, accounting for 84% of the published articles in this field. Additionally, 12% of the articles were published in the area of veterinary sciences, and 85% of the articles fall within the SCI-Expanded category. Conclusions The findings of our study indicate that there are numerous active researchers in the field of artificial intelligence in veterinary medicine and that research in this area is steadily increasing. This bibliometric analysis highlights global trends and significant works in artificial intelligence within veterinary medicine, providing valuable insights into the future directions of research in this field. As the analysis aims solely to identify trends and patterns in the literature, it does not intend to evaluate the applicability of the subject matter.