How do you check a hummingbird for broken bones? Micro-CT scans can reveal hard-to-spot fractures in tiny, injured hummingbirds. Breakthroughs, discoveries, and DIY tips sent six days a week. Like clockwork, ruby-throated hummingbirds () start showing up at wildlife hospitals throughout the eastern United States every spring. The jewel-toned birds are often brought in after crashing into windows or being attacked by domestic cats .

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.

Early disease detection in veterinary care relies on identifying subclinical abnormalities in asymptomatic animals during wellness visits. This study introduces an algorithm designed to distinguish between wellness and other veterinary visits.The purpose of this study is to validate the use of a visit classification algorithm compared to manual classification of veterinary visits by three board-certified veterinarians. Using a dataset of 11,105 clinical visits from 2012 to 2017 involving 655 animals (85.3% canines and 14.7% felines) across 544 U.S. veterinary establishments, the model was trained using a Gradient Boosting Machine model. Three validators were tasked with classifying 400 visits, including both wellness and other types of visits, selected randomly from the same database used for initial algorithm training, aiming to maintain consistency and relevance between the training and application phases; visit classifications were subsequently categorized into "wellness" or "other" based on majority consensus among validators to assess the algorithm's performance in identifying wellness visits. The algorithm demonstrated a specificity of 0.94 (95% CI: 0.91 to 0.96), implying its accuracy in distinguishing non-wellness visits. The algorithm had a sensitivity of 0.86 (95% CI: 0.80 to 0.92), indicating its ability to correctly identify wellness visits as compared to the annotations provided by veterinary experts. The balanced accuracy, calculated as 0.90 (95% CI: 0.87 to 0.93), further confirms the algorithm's overall effectiveness. The algorithm exhibits strong specificity and sensitivity, ensuring accurate identification of a high proportion of wellness visits. Overall, this algorithm holds promise for advancing research on preventive care's role in subclinical disease identification, but prospective studies are needed for validation.

Accurate knowledge selection is critical in knowledge-grounded dialogue systems. Towards a closer look at it, we offer a novel perspective to organize existing literature, i.e., knowledge selection coupled with, after, and before generation. We focus on the third under-explored category of study, which can not only select knowledge accurately in advance, but has the advantage to reduce the learning, adjustment, and interpretation burden of subsequent response generation models, especially LLMs. We propose GATE, a generator-agnostic knowledge selection method, to prepare knowledge for subsequent response generation models by selecting context-related knowledge among different knowledge structures and variable knowledge requirements. Experimental results demonstrate the superiority of GATE, and indicate that knowledge selection before generation is a lightweight yet effective way to facilitate LLMs (e.g., ChatGPT) to generate more informative responses.

Use of artificial intelligence (AI) is increasing in the field of veterinary medicine, but veterinary experts caution that the rush to embrace the technology raises some ethical considerations. "A major difference between veterinary and human medicine is that veterinarians have the ability to euthanize patients--which could be for a variety of medical and financial reasons--so the stakes of diagnoses provided by AI algorithms are very high," says Eli Cohen, associate clinical professor of radiology at NC State's College of Veterinary Medicine. "Human AI products have to be validated prior to coming to market, but currently there is no regulatory oversight for veterinary AI products." In a review for Veterinary Radiology and Ultrasound, Cohen discusses the ethical and legal questions raised by veterinary AI products currently in use. He also highlights key differences between veterinary AI and AI used by human medical doctors.

Use of artificial intelligence (AI) is increasing in the field of veterinary medicine, but veterinary experts caution that the rush to embrace the technology raises some ethical considerations. "A major difference between veterinary and human medicine is that veterinarians have the ability to euthanize patients – which could be for a variety of medical and financial reasons – so the stakes of diagnoses provided by AI algorithms are very high," says Eli Cohen, associate clinical professor of radiology at NC State's College of Veterinary Medicine. "Human AI products have to be validated prior to coming to market, but currently there is no regulatory oversight for veterinary AI products." In a review for Veterinary Radiology and Ultrasound, Cohen discusses the ethical and legal questions raised by veterinary AI products currently in use. He also highlights key differences between veterinary AI and AI used by human medical doctors.

Nestlé Purina Petcare is launching the Petivity Smart Litterbox System, consisting of a smartphone app and litterbox monitor that captures and transforms behavioral data into actionable insights that help owners proactively care for their cat. The smart monitor helps owners provide a new standard of care for their cats by using artificial intelligence to learn each cat's unique litterbox patterns and identify subtle but meaningful changes in weight, frequency, waste type and elimination schedule. "The Petivity Smart Litterbox System allows cat owners to get personalized insights to their cat's litterbox usage," said Dr. Avi Shaprut, DVM, Purina veterinarian. "While the device is not intended to diagnose, treat, mitigate or cure any conditions, it can help detect changes that can be early signs of health conditions like diabetes, kidney disease, hyperthyroidism, urinary tract infections and obesity, allowing cat owners to proactively seek out veterinary care earlier and unlock better outcomes." Using artificial intelligence developed by a team of Purina pet and data experts, the Petivity Smart Litterbox System detects meaningful changes that indicate health conditions that may require a veterinarian's attention or diagnosis.

Leptospirosis, a disease that dogs can get from drinking water contaminated with Leptospira bacteria, can cause kidney failure, liver disease and severe bleeding into the lungs. Early detection of the disease is crucial and may mean the difference between life and death. Veterinarians and researchers at the University of California, Davis, School of Veterinary Medicine have discovered a technique to predict leptospirosis in dogs through the use of artificial intelligence. After many months of testing various models, the team has developed one that outperformed traditional testing methods and provided accurate early detection of the disease. The groundbreaking discovery was published in the Journal of Veterinary Diagnostic Investigation.

If artificial intelligence is the future, then the future is now. This pandemic has shown us just how fast artificial intelligence, or AI, works and what it can do in so many different ways. Right from the start, AI has helped us learn about SARS-CoV-2, the virus that causes COVID-19 infections. It's helped scientists analyse the virus' genetic information -- its DNA -- at speed. DNA is the stuff that makes the virus, indeed any living thing, what it is.

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.