Explainable Artificial Intelligence (AI) and Clinical Reliability in Histology

Artificial intelligence (AI) is transforming histological tissue analysis in veterinary sciences, but the limited interpretability of deep learning models remains a major barrier to clinical adoption. This chapter examines explainable AI (XAI) techniques, including Grad-CAM, LIME, SHAP, and attention mechanisms, and their role in improving diagnostic transparency and reliability in veterinary histopathology. Drawing on evidence from canine mammary tumours, feline lymphoma, equine pulmonary disease, and comparative computational pathology, it explores how XAI supports whole-slide image analysis, quality assurance, and regulatory compliance by highlighting the morphological features underlying AI predictions. The chapter concludes with practical recommendations and future research priorities focused on standardized evaluation, clinical validation, and robust data governance to strengthen trust in AI-assisted veterinary diagnostics.

Ҳали таржима қилинмаган