Original title: Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications
Fernando Tecles, Julian J Arense-Gonzalo, Alberto Muñoz-Prieto, Asta Tvarijonaviciute +3 · September 2026
Veterinary medicine needs better ways to analyze patient data. While human medicine has rapidly adopted artificial intelligence, animal care has been slower to catch up. The authors of this paper reviewed recent advances in using machine learning with laboratory biomarkers, which are measurable clues found in biological samples. Machine learning works by scanning large volumes of laboratory data to spot clinical patterns and connections that humans might miss. The researchers examined how these computer algorithms help companion animalsPets such as dogs and cats., specifically dogs and cats, as well as farm animalsLivestock such as cows and pigs kept for food production., specifically bovine and porcine species. They found that machine learning can help veterinarians diagnose and monitor specific diseases. For farm animals, it also helps track and boost productive performance. Finally, looking at human medicine suggests future uses, such as better laboratory management and improved tracking of treatments.
You are packing a suitcase for a weekend trip. You have a pile of clothes but only space for a few, so you must choose the most essential ones. For a veterinarian, a sick animal's lab results are like that pile—too many numbers to consider all at once, and the key ones are not obvious. Here, machine learning helps pick out which blood tests and biomarkers are most important for a correct diagnosis, like choosing the right clothes for the trip.
For pet owners, this means earlier and more accurate disease detection from routine bloodwork. Farmers could use the same technology to monitor herd health and boost productivity. Veterinarians gain a tool that finds patterns in lab data that humans might miss.
This paper is a narrative review, not original research, so it does not present new experimental results or compare specific ML methods. The applications discussed are based on existing studies and extrapolation from human medicine, not yet fully validated in veterinary practice. The review is limited to four species (dogs, cats, cattle, pigs) and may not capture progress in other animals or recent technical developments.
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Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications — Fernando Tecles, Julian J Arense-Gonzalo, Alberto Muñoz-Prieto et al. — The veterinary quarterly — doi:10.1080/01652176.2026.2715555
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.