ArticleBMC veterinary research2025
The application of artificial intelligence in veterinary oncology: a scoping review.
Article in BMC veterinary research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.The veterinary quarterly · 2026Article
- Radiation in Veterinary Practice: Paradigm Shift Toward Precision and Curative Approaches.Life (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe application of artificial intelligence (AI) in veterinary oncology is rapidly expanding, mirroring its advancements in human medicine. This field is uniquely positioned to offer bi-directional insights due to the spontaneous development of cancers in companion animals that are similar to those in humans. However, a comprehensive understanding of the current research landscape is lacking. This scoping review was conducted to systematically map the literature on AI in veterinary oncology, identifying the clinical applications, techniques, and data sources being utilized, as well as the major challenges hindering clinical translation.
resultsThe review included 69 studies, revealing a field with a strong focus on diagnostic applications in canine patients, particularly for common tumor types such as lymphomas, (sub-)cutaneous and mammary tumors. The most mature applications involve image-based diagnostics, including digital pathology and radiomics, where deep learning models have demonstrated high performance in tasks like tumor grading and non-invasive characterization. While emerging applications in treatment planning and multimodal data fusion show great promise, the overall field is limited by a pervasive reliance on small, single-source datasets and a lack of external and prospective validation.
conclusionsThe application of AI in veterinary oncology has produced powerful proof-of-concept models, particularly in diagnostics, with a clear potential to augment clinical practice. However, the path from research to clinical implementation is hindered by fundamental challenges, including the data bottleneck and validation gap. To fulfill its transformative potential, the field must prioritize a shift from isolated studies to collaborative, large-scale research efforts that generate standardized, public datasets and emphasize rigorous external validation. By doing so, the community can ensure the development of generalizable AI models that will truly improve cancer care for veterinary patients.
Indexed as
Identifiers
What Socratic 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 Socratic graph.