Digital Pathology and Artificial Intelligence in Veterinary Diagnostic Pathology: Implementation, Validation, and Emerging Applications

Abstract

Digital pathology, driven by whole-slide imaging (WSI), digital slide management, and computational image analysis, is increasingly transforming veterinary diagnostic pathology. The integration of artificial intelligence (AI), including machine learning and deep learning, provides opportunities to automate or assist tasks such as lesion detection, tissue segmentation, mitotic figure identification, immunohistochemistry quantification, and morphometric analysis. However, successful implementation requires more than scanner acquisition or algorithm development. This chapter presents a practice-oriented overview of digital pathology and AI in veterinary diagnostic pathology, emphasizing the complete workflow from tissue processing and slide preparation to scanning, digital viewing, reporting, validation, and quality management. Major pre-analytic, analytic, and post-analytic sources of error are discussed, together with principles for validating whole-slide imaging for primary diagnosis and maintaining ongoing quality assurance. The chapter also describes major AI task types, annotation and ground-truth challenges, performance evaluation, external validation, model monitoring, and responsible deployment. Particular attention is given to veterinary-specific challenges, including species and breed heterogeneity, stain variability, domain shift, limited annotated datasets, data governance, and automation bias. Emerging approaches such as foundation models, self-supervised learning, multimodal integration, and federated learning are also considered. Overall, effective digital pathology adoption depends on rigorous validation, continuous quality management, appropriate interpretation, and pathologist-led diagnostic accountability. Keywords Keywords: Digital pathology; whole-slide imaging; artificial intelligence; computational pathology; veterinary diagnostic pathology; machine learning;