Document Type : Review Article
Authors
1
Clinician and Resident of Radiology, Department of Clinical Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran
2
Doctor of Veterinary Medicine Student, Razi University, Kermanshah, Iran.
3
Clinician and Resident of Large Animal Internal Medicine, Department of Clinical Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran
4
Department of Clinical Sciences, Islamic Azad University, Shabestar Branch, Iran.
5
Pathologist, Board-Certified Clinical Pathology,Oncopathology Researcher ; Cancer, Environmental and Petroleum Pollutants Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Pathologist, Board-Certified
10.66224/ari.2026.372493.4084
Abstract
Artificial intelligence, as one of the most influential technologies of the present era, has significant potential to transform veterinary sciences, especially in pathology and clinical sciences. Despite rapid advances in machine learning and deep learning algorithms, their practical application in real clinical settings poses numerous complex challenges. This prospective review aims to comprehensively identify and analyze the challenges ahead and outline the prospects for the application of artificial intelligence in diagnostic pathology and veterinary clinical sciences. A comprehensive search of articles in the reputable databases PubMed, Scopus, Web of Science, and Google Scholar was conducted between January 2020 and December 2025 using combined keywords related to artificial intelligence and veterinary medicine. The identified challenges were categorized into three main areas: 1) technical and infrastructural challenges (including lack of standardized and labeled data, high species and breed diversity, hardware limitations, and the “black box” nature of algorithms), 2) clinical and professional challenges (including lack of independent clinical validation, difficulty in integrating with clinical workflow, professional resistance, and ambiguity in civil liability), and 3) ethical and legal challenges (including data privacy, inequality in access, data bias, and gaps in regulatory frameworks). In contrast, future perspectives included the move towards precision and predictive medicine through the integration of AI with new technologies (pathomics, IoT, augmented reality), the evolution of the veterinarian’s role as a smart health manager, and improvements to the veterinary health system through early diagnosis and evidence-based management.
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