ARTIFICIAL INTELLIGENCE APPLIED TO VETERINARY DIAGNOSIS: ACCURACY, ETHICS, AND THE CLINICAL FUTURE

Abstract

This chapter aims to analyze the applications of Artificial Intelligence (AI) in veterinary diagnosis, highlighting improvements in accuracy, ethical implications, and future clinical perspectives. The methodology consisted of a narrative review of national and international scientific literature, including recent studies on machine learning, convolutional neural networks, and clinical decision support systems applied to veterinary medicine. The findings indicate that algorithms trained on large datasets have enhanced diagnostic accuracy in imaging exams, clinical pathology, and remote animal monitoring, reducing interpretative variability and optimizing clinical workflow. However, challenges remain regarding data quality, algorithmic bias, professional accountability, and data protection. It is concluded that AI represents a promising complementary tool capable of strengthening evidence-based veterinary practice, provided it is integrated with ethical guidelines, rigorous scientific validation, and continuous professional training.