While the global integration of artificial intelligence (AI) into veterinary medicine is accelerating, its adoption dynamics in major markets such as China remain uncharacterized. This paper presents the first exploratory analysis of AI perception and adoption among veterinary professionals in China, based on a cross-sectional survey of 455 practitioners conducted in mid-2025. We identify a distinct "adoption paradox": although 71.0% of respondents have incorporated AI into their workflows, 44.6% of these active users report low familiarity with the technology. In contrast to the administrative-focused patterns observed in North America, adoption in China is practitioner-driven and centers on core clinical tasks, such as disease diagnosis (50.1%) and prescription calculation (44.8%). However, concerns regarding reliability and accuracy remain the primary barrier (54.3%), coexisting with a strong consensus (93.8%) for regulatory oversight. These findings suggest a unique "inside-out" integration model in China, characterized by high clinical utility but restricted by an "interpretability gap," underscoring the need for specialized tools and robust regulatory frameworks to safely harness AI's potential in this expanding market.

This article had its genesis when co-author Ed's dog, Sparkle, was treated for pneumonia in the summer of 2024. Ed, a mathematician and chair of the Alliance for Data Science Professionals, was intrigued by the surgery's use of data in Sparkle's treatment and decided to find out more about the use of data and AI in veterinary medicine. His exploration led to a guest appearance on the Vet Voices on Air podcast hosted by co-author Robyn. She is a registered veterinary nurse (RVN) and the director of Veterinary Voices UK . Inspired by that conversation, this article explores the ways veterinary professionals are currently applying data science principles and how professions adapt and evolve in the face of these developments.

Breakthroughs, discoveries, and DIY tips sent every weekday. Any pet parent knows that our furry friends can get into accidents. While some like rolling around in the mud are mainly a nuisance, ingesting something that they shouldn't can be very dangerous. In a study published August 18 in the journal Frontiers in Veterinary Science, Doctor Jake Johnson, a cardiology resident at North Carolina State University's College of Veterinary Medicine, presents a case study of a chihuahua that accidentally ingested cocaine. Ahead of the study's publication, the team at Frontiers conducted this Q&A with Dr. Johnson.

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.

Dogs have a sense of the basic way objects should behave, according to scientists, who say they stare longer if a computer animation breaks the laws of physics. Humans use a process known as'contact causality' from an early age to make sense of the physical environment, but little is known about the processes that non-primate animals use to make sense of the world and how things work. To better understand this in dogs, a team at the University of Veterinary Medicine, Vienna, adapted an eye tracking system used on human infants. Dogs were presented with realistic 3D animations of balls that obey and break Newton's basic laws of physics, and tracked their pupil dilation and attention span. The animals tracked the movements of balls closely throughout the study, but pupils were larger when objects in the animations broke the laws of physics.

When 3D animated balls on a computer screen defy certain laws of physics, dogs act in a way that suggests they feel like their eyes are deceiving them. Pet dogs stare for longer and their pupils widen if virtual balls start rolling on their own rather than being set in motion by a collision with another ball. This suggests that the animals are surprised that the balls didn't move the way they had expected them to, says Christoph Völter at the University of Veterinary Medicine, Vienna. "This is the starting point for learning," says Völter. "You have expectations about the environment – regularities in your environment that are connected to physics – and then something happens that doesn't fit. And now you pay attention. And now you try to see what's going on."

Through AI platforms, computers can learn how to mimic human thought processes and cognitive functions, but with increased speed and learning capacity. Artificial intelligence has several subdisciplines, including machine learning, unsupervised learning, and deep learning. The versatility of AI makes it useful in a variety of medical disciplines. In human medicine, AI has already found applications in areas such as drug design, anesthesiology, cardiology, radiology, oncology, and infectious disease management. In general veterinary practice, protocols for vaccination, parasite prevention, and many aspects of wellness care are well-established.

Artificial intelligence has several subdisciplines, including machine learning, unsupervised learning, and deep learning.

Upon his retirement from veterinary practice--the last 2 decades of which he spent in specialty care where he helped to establish the model of a referral hospital--Neil Shaw, DVM, DACVIM, started thinking about how treatment protocols in general practices could be improved. His primary concern: how to scale what is done in specialty practices for use in general practices. Preventive care protocols are well established in general practice, Shaw says, but "models for treating common illnesses and injuries in primary care practice really have not been well established." "Not all cases need to be referred," he tells Adam Christman, DVM, MBA, in this episode of the Vet Blast Podcast. And he saw technology as the only way to accomplish that goal.