ADVANCEMENTS IN VETERINARY PATHOLOGY: USING AI FOR AUTOMATING DIAGNOSIS OF INFECTIOUS DISEASES IN ANIMALS

Abstract

The integration of artificial intelligence (AI) into veterinary pathology represents a transformative advancement in the automated diagnosis of infectious diseases in animals. This study systematically evaluated AI-driven diagnostic frameworks using a mixed experimental approach that combined quantitative performance assessment with qualitative expert validation. The results demonstrate that AI-based models, particularly those leveraging deep learning and multimodal data integration, achieve high diagnostic accuracy across diverse animal species and pathogen classes. Automated analysis of histopathological images and laboratory data significantly reduced diagnostic turnaround time compared with conventional workflows, while maintaining strong concordance with expert veterinary pathologists. The findings further indicate that AI systems exhibit robust performance under heterogeneous clinical conditions, including varying infection severity, data noise, and interspecies variability. Scalability analyses revealed that diagnostic throughput increased efficiently with case volume, highlighting the feasibility of deploying AI tools in high-demand veterinary diagnostic settings. Importantly, qualitative expert evaluations confirmed that AI-generated outputs were clinically interpretable and supportive of decision-making rather than disruptive to established diagnostic practices. Despite these strengths, the results also underscore persistent challenges related to data standardization, limited availability of large annotated veterinary datasets, and the need for rigorous validation across broader animal populations. Overall, this study provides compelling evidence that AI-assisted diagnostic systems can enhance accuracy, efficiency, and consistency in veterinary infectious disease diagnosis, supporting improved animal health outcomes and contributing to broader public health and zoonotic disease surveillance efforts.