State-of-the-Art Artificial Intelligence Frameworks and Models for Diagnostic Veterinary Anatomy.

Background

Artificial intelligence (AI) is increasingly being used in diagnostic veterinary anatomy to assist with medical-image interpretation, anatomical segmentation, disease recognition, and quantitative morphometric measurements. Even so, evidence specific to this area remains fragmented, particularly with regard to the performance of individual AI frameworks, the way they have been validated, and their practical relevance to veterinary diagnostic anatomy.

Purpose

This review critically examines current AI frameworks and models applied to diagnostic veterinary anatomy. Particular attention is given to how these approaches work, where they have been used, their reported diagnostic performance, their main strengths and limitations, the extent of validation, and areas that still require investigation.

Methods

Relevant studies were identified by searching PubMed and Scopus, and the retrieved literature was assessed to identify recent developments in AI-assisted diagnostic veterinary anatomy. After screening and eligibility assessment, 45 primary investigations were retained for the final synthesis. The review covers convolutional neural networks, Vision Transformer-based methods, segmentation frameworks, object-detection models, generative models, self-supervised learning approaches, and other AI techniques used for veterinary diagnostic and anatomical tasks.

Results

Across radiography, computed tomography, magnetic resonance imaging, ultrasonography, histopathology, and other anatomical imaging settings, several studies reported high internal performance for AI methods. The included investigations examined image classification, anatomical segmentation and localization, object detection, quantitative morphometry, disease detection, anatomical identification, and image enhancement. Nevertheless, the studies differed markedly in species, dataset size, design, performance measures, and validation strategy. Several models performed strongly on the datasets used for development, but independent external testing and prospective clinical evaluation were uncommon.

Conclusions

Artificial intelligence has clear potential to support diagnostic veterinary anatomy in both research and clinical settings. Broader clinical use, however, will require larger and more representative datasets, consistent validation procedures, explainable methods, independent external assessment, and prospective clinical studies. Further progress in multimodal learning and clinically tested models may make AI more useful in veterinary anatomical diagnosis and precision veterinary medicine.