It seems the provided document URL does not point to a valid or accessible PDF file, as the link appears to be a temporary Azure blob storage URL with an expiration date far in the future. Without access to the actual document content, I cannot perform the OCR and structuring task.

Shiri, Fatemeh and Shahinzadeh, Amin and Heidari, Arya and Safaie, Amirreza and Ghanbari, Sina and Safari, Narges and Atazadeh, Amir Hossein and Ahmadi Simab, Pouria (2026) It seems the provided document URL does not point to a valid or accessible PDF file, as the link appears to be a temporary Azure blob storage URL with an expiration date far in the future. Without access to the actual document content, I cannot perform the OCR and structuring task. World's Veterinary Journal. 750 - 764. ISSN 23224568

Text WVJ16(2)750-765,2026 (1).pdf - Published Version Download (1MB)

Abstract

Artificial intelligence (AI) and machine learning are rapidly transforming companion animal veterinary medicine. The present narrative review examined current applications, challenges, and future directions for AI in the care of dogs, cats, and other companion animals. The current study is based on a structured literature search across PubMed/MEDLINE, Scopus, and IEEE Xplore from January 2015 to April 2026, with clearly defined inclusion and exclusion criteria to ensure transparency and reproducibility. In diagnostic imaging, computer vision algorithms improve the speed and accuracy of interpreting radiographs, ultrasound, and dermatoscopic images, helping detect conditions such as hip dislocation, osteoarthritis, foreign bodies, skin lesions, and parasites. In digital pathology, AI aids in identifying abnormal cells from blood and histology slides, supporting diagnoses such as lymphoma and anemia. Wearable devices with AI algorithms continuously monitor physiological data, including activity, sleep, and early signs of pain. Natural language processing (NLP) extracts information from electronic health records to facilitate epidemiological studies. Despite this promise, several challenges remain. Technical hurdles include the need for large, high-quality annotated datasets, which are harder and more costly to obtain than in human medicine, and the high genetic diversity among dog and cat breeds, which makes it difficult to develop generalizable algorithms. Ethical and legal issues involve liability for diagnostic errors, data privacy, and algorithmic bias, requiring new regulatory frameworks. Practical barriers include high costs, integration into clinical workflows, and the need to train veterinarians to effectively use and critically evaluate AI outputs. Future progress depends on close collaboration among AI specialists, veterinarians, biologists, and industry. Priorities include developing cost-effective, user-friendly tools; creating internationally shared, standardized databases; and establishing ethical and professional guidelines. Blockchain may serve as a trust layer for recording and verifying data access events, rather than as a primary repository for large-scale medical data. Ultimately, AI is expected to act as a powerful assistant, not a replacement for veterinary expertise, enabling earlier diagnoses, personalized treatments, improved preventive care, and a better quality of life for companion animals and their owners. © The Author(s) 2026 Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Item Type:	Article
Keywords:	Artificial intelligence; Companion animal; Diagnostic imaging; Machine learning; Veterinary medicine
Subjects:	S Agriculture > SF Animal culture
Divisions:	World's Veterinary Journal (WVJ)
Page Range:	750 - 764
Journal or Publication Title:	World's Veterinary Journal
Journal Index:	Scopus
Volume:	16
Number:	2
Publisher:	Scienceline Publication
Identification Number:	https://doi.org/10.54203/scil.2026.wvj70
ISSN:	23224568
Depositing User:	Dr. Alireza Sadeghi
URI:	https://eprints.science-line.com/id/eprint/1703

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