ArticleVeterinary medicine and science2026
Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography.
Article in Veterinary medicine and science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly used in veterinary diagnostic imaging, particularly in thoracic radiology, to support image interpretation.
objectiveThe aim of this study was to evaluate research trends in AI applications for small animal thoracic radiography through bibliometric analysis, identifying key contributors, leading journals, thematic focuses and collaboration patterns.
methodsStudies published between 2018 and 2025 were retrieved from the Web of Science (WoS) and PubMed databases using predefined Boolean search terms. After duplicate removal and screening based on inclusion and exclusion criteria, 27 articles were included. Data were analysed using RStudio (Biblioshiny) to evaluate publication trends, citation impact, scientific productivity, collaboration networks and thematic structures.
resultsPublications showed an increasing trend, with an annual growth rate of 10.41% and a peak in 2023 (n = 8). Studies published in 2020 showed the highest citation impact, averaging 46.33 citations. T. Banzato emerged as the most prolific author, while S. Burti authored the most cited study. The United States (n = 32) and Italy (n = 23) were the leading contributing countries. Thematic analyses demonstrated a primary focus on convolutional neural network (CNN)- and deep learning-based automated detection of cardiomegaly, pleural effusion and pulmonary diseases in dogs, reflecting the clinically driven nature of early studies. A significant positive correlation (p < 0.001) was found between WoS citation counts and those from other databases, whereas other bibliometric indicators showed no significant associations.
conclusionsAI shows considerable potential to support diagnostic processes in veterinary thoracic radiology; however, dataset heterogeneity and species diversity remain major limitations, highlighting the need for methodological standardization and stronger international collaboration.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.