Artificial Intelligence Applications for Veterinary Medicine

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Veterinary Clinical Studies".

Deadline for manuscript submissions: 1 April 2026 | Viewed by 613

Special Issue Editor


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Guest Editor
1. Veterinary and Animal Science Research Centre (CECAV), Vila Real, Portugal
2. Department of Animal Science, University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal
3. Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), Vila Real, Portugal
Interests: clinical anatomy; applied morphology; osteoarthritis; pain management; software development for analysis of medical imaging; screening heritable diseases
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a transformative tool in veterinary medicine, offering innovative solutions across diagnostics, treatment planning, disease surveillance, and animal health management. Machine learning algorithms, image recognition, and predictive analytics are increasingly applied to enhance diagnostic accuracy, optimize therapeutic interventions, and streamline clinical decision-making. AI tools are being integrated into imaging modalities such as radiography and ultrasound, enabling automated lesion detection and classification. Furthermore, natural language processing is facilitating the extraction of clinical insights from unstructured veterinary records, contributing to evidence-based practice.

The aim of this Special Issue is to bring together the latest findings concerning the role of AI in veterinary science and animal healthcare. Original research papers and literature reviews from different research areas, such as diagnostic imaging and analysis, pathology, epidemiology, animal behavior, telemedicine, veterinary robotics, pet health monitoring devices, personalized treatment plans, supply chain and inventory management, veterinary drug discovery, and chatbots for pet owners, are encouraged. These contributions will illustrate how AI is being employed to solve complex veterinary challenges, ranging from diagnostic automation to disease prevention strategies. Additional topics and interdisciplinary studies regarding the ethical, regulatory, and economic aspects of AI adoption in veterinary practice will also be considered.

Dr. Sofia Alves-Pimenta
Guest Editor

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Keywords

  • diagnostic imaging
  • disease surveillance and outbreak prediction
  • predictive analytics for disease
  • clinical decision support systems

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Published Papers (1 paper)

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Review

16 pages, 1827 KB  
Review
Disease Prediction in Cattle: A Mixed-Methods Review of Predictive Modeling Studies
by Lilli Heinen, Robert L. Larson and Brad J. White
Animals 2025, 15(17), 2481; https://doi.org/10.3390/ani15172481 - 23 Aug 2025
Viewed by 241
Abstract
Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data [...] Read more.
Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data types utilized to train models. This narrative review aims to describe predictive model performance in greater detail across various disease outcomes, input data types, and algorithms with a specific focus on accuracy, sensitivity, specificity, and positive and negative predictive values. A secondary goal is to address important areas for consideration for future work in the beef cattle sector. In total, 19 articles were included. Broad categories of disease were covered, including respiratory disease, bovine tuberculosis, and others. Various input data types were reported, including demographic data, images, and laboratory test results, among others. Several algorithms were utilized, including neural networks, linear models, and others. Accuracy, sensitivity, and specificity values ranged widely across disease outcome and algorithm categories. Negative predictive values were greater than positive predictive values for most disease outcomes. This review highlights the importance of utilizing several performance metrics and concludes that future work should address prevalence of outcomes and class-imbalanced data. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Veterinary Medicine)
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