Exploring the Use of Artificial Intelligence in Early Detection and Diagnosis of Diseases in Veterinary Medicine

Abstract

Artificial intelligence is increasingly being integrated into veterinary medicine to enhance diagnostic accuracy, disease prediction, and clinical decision support. This study systematically evaluated the performance of AI-driven models using a mixed-method experimental approach that combined quantitative analysis of large-scale veterinary datasets with qualitative expert validation. Results from multiple experimental evaluations demonstrated consistently high diagnostic accuracy, sensitivity, and specificity across veterinary specialties, disease categories, and species. Convolutional neural network–based imaging models showed superior performance in complex diagnostic tasks, while predictive analytics effectively forecasted disease onset and epidemiological trends. Graphical and tabular analyses revealed strong model robustness under varying noise conditions, improved learning efficiency with increasing data volume, and reduced false-positive rates compared to conventional approaches. The findings confirm that AI-based systems can process multimodal veterinary data efficiently, identify subtle diagnostic patterns, and support earlier and more precise clinical interventions. Importantly, the study highlights that AI serves as a reliable decision-support tool that complements veterinary expertise rather than replacing it. While challenges related to data availability, standardization, and ethical considerations remain, the results provide strong evidence that artificial intelligence can play a pivotal role in advancing preventive, personalized, and data-driven veterinary healthcare.