A Inteligência Artificial na medicina veterinária: avanços tecnológicos, aplicabilidade diagnóstica e impactos na saúde única

Abstract

This article investigates the integration of artificial intelligence in Veterinary Medicine, analyzing its theoretical foundations, diagnostic applicability, epidemiological impacts, and ethical dilemmas. The general objective is to understand how intelligent technologies fill clinical and operational gaps, acting as decision support tools for veterinarians. The methodology is based on an exploratory and qualitative bibliographic review, analyzing recent academic publications, technology industry reports, and market innovations. The results indicate that artificial intelligence, through machine learning and convolutional neural networks, significantly optimizes diagnostic accuracy in imaging and laboratory tests, allows the prediction of zoonotic epidemiological outbreaks, and revolutionizes animal monitoring and welfare in agribusiness. Emerging platforms and large language models have transformed the clinical workflow, automating everything from anamnesis to ophthalmological screening in remote regions. It is concluded that artificial intelligence does not replace human clinical reasoning but acts as an essential complement that increases the speed and accuracy of diagnoses. However, regulatory challenges, risks of algorithmic hallucination, and the need for continuous ethical training still require attention and development.