Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography

Area of Science:

• Veterinary Radiology
• Artificial Intelligence
• Bibliometrics

Background:

• Artificial intelligence (AI) is increasingly integrated into veterinary diagnostic imaging, specifically for thoracic radiology interpretation.
• AI tools aid veterinarians in analyzing complex thoracic radiographs.

Purpose of the Study:

• To conduct a bibliometric analysis of research trends in AI applications for small animal thoracic radiography.
• Identify key researchers, journals, research themes, and collaboration patterns in this field.

Main Methods:

• Searched Web of Science and PubMed databases for studies from 2018-2025.
• Included 27 articles after screening, analyzing publication trends, citation impact, and collaboration networks using RStudio (Biblioshiny).

Main Results:

• A consistent increase in publications was observed, with a 10.41% annual growth rate and a peak in 2023.
• The United States and Italy led in contributions, with a primary focus on deep learning for detecting cardiomegaly, pleural effusion, and pulmonary diseases in dogs.
• Studies from 2020 had the highest citation impact, averaging 46.33 citations.

Conclusions:

• AI holds significant potential for veterinary thoracic radiology diagnostics.
• Limitations include dataset heterogeneity and species diversity, necessitating methodological standardization and enhanced international collaboration.