In a world where roughly 60 % of emerging human infectious diseases originate from animals (Taylor et al., 2001), the shortcomings of traditional veterinary diagnostic techniques, especially in interpreting complex anatomical variations, have become increasingly evident. Veterinarians today are confronted with a challenging environment marked by rising zoonotic threats, heavy workloads, and the persistent difficulty of diagnosing conditions stemming from subtle anatomical changes. Artificial intelligence (AI) is emerging not as a substitute for veterinary expertise but as a robust collaborator that enhances our ability to understand the intricate details of animal anatomy. The creation and testing of these AI tools have greatly improved because of animal anatomical teaching models. These models offer important, consistent ways to train and improve diagnostic algorithms (Choudhary and Sarkar, 2025). For instance, AI-assisted interpretation of radiographs can reduce diagnostic errors by up to 30 % in small animal practice, demonstrating superhuman precision (Lakhani and Sundaram, 2017, Zhang et al., 2024). More significantly, recent studies confirm that AI's potential in animal anatomy goes far beyond basic detection, offering a new paradigm for understanding morphological structures and their pathological changes (Choudhary et al., 2025).

The consequences of wrong diagnosis in anatomy are especially serious when looking at the whole population. The 2018 African swine fever outbreak in Asia, which caused economic losses over $20 billion, showed how traditional ways of monitoring diseases fail to stop fast-spreading illnesses (Food and Agriculture Organization of the United Nations FAO,2020). Now, AI systems are changing things a lot; machine learning models use satellite images, climate information, and farm data to predict disease outbreaks up to weeks before they happen. These AI tools in anatomical imaging are changing how we do diagnosis and disease tracking, connecting individual animal health with global public health (Vickram et al., 2025). Still, not many people are using these AI tools. Less than 15 % of veterinary clinics use them, mainly because of high costs, the difficulty in understanding complex algorithms, and the need for better connection with basic knowledge of anatomy.

This review article will provide the information about how AI is changing veterinary medicine in two broader perspectives. First, it helps doctors to understand medical images and records better, making diagnosis more accurate. Second, it's being used to build smart systems those can track zoonotic diseases transmit between animals and humans. It will address the role of technology itself, but also the real challenges when trying to use it, like ethical issues and gap between those who have access to new techniques and those who don’t. Due to climate changes emergence of zoonotic diseases and their spread is many folds quicker as compared to other historical outbreaks (Carlson et al., 2022). Hence using AI carefully and smartly could be the ways to make the One Health idea real time implementation. This would help to make this planet safer for the animals, people, and the environment, we all live in. In this article the available data on AI will be reviewed in following subheadings: (1) AI in Veterinary Anatomical Diagnostics, (2) AI in Zoonotic diseases monitoring, (3) Challenges and (4) Future directions.