ReviewThe veterinary quarterly2026
Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.
Review in The veterinary quarterly, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence has been a revolution in human medicine in the last years. Machine learning (ML), a subset of artificial intelligence, has allowed the use of a large amount of data to search for associations of clinical significance. Although the use of ML with laboratory biomarkers in veterinary medicine is limited in comparison with the human side, the number of studies published in this field is increasing in recent years. This review addresses some basic concepts and gives a general overview of the use of ML in the area of biomarkers in veterinary medicine. It is especially focused on two species of each companion (dogs and cats) and farm (bovine and porcine) animals, and provides information about the advances made in the last years, indicating how ML using laboratory data can contribute to the diagnosis and monitoring of selected diseases and, in case of farm animals, also to better control and improve their productive performance. In addition, possible new applications such as optimizing laboratory management, refining diagnostics or improving treatment monitoring based on the experience in humans are outlined. This narrative review can contribute to a better knowledge about the possible use of ML in laboratory biomarkers in veterinary and animal sciences for current and future applications.
Indexed as
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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.