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Artificial intelligence and machine learning are increasingly shaping the future of small animal veterinary medicine, particularly through predictive modeling that estimates disease risk. This article introduces key concepts underlying disease prediction and precision veterinary medicine and explains how diverse data sources including electronic medical records, insurance claims, wearable devices, and environmental datasets support predictive analytics. The article reviews common modeling approaches, emerging clinical applications, and the role of companion animals as sentinels in a One Health framework. It also examines practical limitations, potential biases, and ethical considerations, emphasizing that predictive tools should complement, not replace, clinical expertise in veterinary practice.
As veterinary artificial intelligence (AI) tools become more available, it is vital to ensure that they genuinely promote patient well-being and avoid harm. This article examines the ethics of AI in small animal veterinary medicine, considering both "machine" and "human" aspects. The article then outlines appropriate accountability responsibilities for veterinary practitioners, as well as for AI developers and purveyors, hospital and clinic managers, and professional veterinary bodies. Understanding these ethical issues and accountability measures should help steer the creation, maintenance, and use of AI tools toward meeting the core veterinary medical obligation of improving animal health and welfare.
Humane euthanasia is one of the most sacred responsibilities of the veterinary profession. Veterinary professionals should always strive to demonstrate a balance of medical knowledge and emotional sensitivity during euthanasia conversations and appointments; the biggest challenge of this balancing act stems from the fact that veterinarians are not only advocating for the physical welfare of their patient but also for the emotional welfare of the pet's family. Above all, showing affection for the pet, acknowledging the accomplishments of the family in the pet's care, and achieving a peaceful transition should be seen as a final victory of the human-animal bond.
Computer vision (CV) is an emerging application of artificial intelligence (AI) with growing relevance to veterinary cytology. This review provides a foundational overview of CV concepts and model architectures and summarizes current research progress in small animal cytology, including blood smear examination. The author discusses trends in scientific validation, industrial implementation, and regulatory oversight, highlighting gaps in transparency and standardization. Educational needs, professional guidelines, and concerns regarding deskilling and workforce impact are also discussed. Together, this review aims to support informed adoption of CV tools while emphasizing the importance of validation, professional oversight, and AI literacy in veterinary practice.
Artificial intelligence (AI) and machine learning (ML) are increasingly being explored and adopted in veterinary medicine. This article provides veterinary professionals with a foundational understanding of AI and ML concepts, including the distinction among AI, ML, and deep learning; the major learning paradigms (supervised, unsupervised, and reinforcement learning); and common algorithms and architectures such as decision trees, random forests, support vector machines, and neural networks. Key terminology, model evaluation metrics, and practical considerations for clinical implementation are discussed. Understanding these fundamental concepts will prepare veterinary professionals to evaluate, adopt, and contribute to the development of AI-based tools in their practice.
Artificial intelligence (AI) tools are rapidly entering veterinary medicine, yet clinicians often lack frameworks to evaluate their performance. This article provides a practical guide to understanding AI evaluation metrics, including sensitivity, specificity, precision, recall, and area under the receiver operating characteristic curve, and explains why accuracy alone is insufficient. We address the critical role of interannotator agreement in establishing performance ceilings, the importance of external validation, and modality-specific evaluation considerations for AI scribes, digital imaging, and pathology applications. In the absence of regulatory oversight, veterinary professionals must develop evaluation literacy to make informed decisions about AI adoption.
Increasingly, many pets have become family. Sometimes, they are the family's favorite members, and they have no trouble admitting it. Technically, in many places, pets are legally considered property; however now many are considered family, sleeping in bed, eating better than their families, the reason a couple gets together, their first "child," as some have phrased it. While we take care of the patient, we must consider the quality of life of the pet and that of its family. At the end of the patient's life, it is our honor to help them pass peacefully.
The initial emergency assessment and stabilization of patients with respiratory distress requires an alternative diagnostic and therapeutic approach. A combination of initial therapeutic interventions to relieve signs of respiratory distress or stabilize the patient, including anxiolytics and oxygen therapy, along with diagnostics such as point-of-care ultrasound, is often helpful. Definitive diagnostics and therapeutics can then be tailored to the patient once the source of the respiratory distress is localized broadly to the upper airway, pulmonary parenchyma, pleural space, or cardiac structures.
Artificial intelligence (AI) serves as a decision support tool, not a replacement for clinical judgment, when used to interpret radiological images. Veterinarians retain full professional accountability for all diagnoses and treatment decisions, regardless of AI involvement. Transparency is essential: if you cannot explain to clients in understandable terms how an AI system works and its limitations, it should not be used in practice. Successful implementation requires following established best practices, including comprehensive team training, maintaining traditional diagnostic skills, and establishing quality assurance protocols.
Clinical decision-making, including decisions about diagnosis and treatment options, are an important part of veterinary practice. While it is now recognised that the human brain has access to two main pathways to decision making these are subject to bias and limitations of working memory. This article will look at how the principles of evidence-based veterinary medicine and tools using artificial intelligence can be used to support clinical decision making.
Artificial intelligence (AI) tools are rapidly being adopted in veterinary medicine. Unlike human medicine, where the Food and Drug Administration has established comprehensive pre-market approval pathways for AI-enabled medical devices, veterinary AI tools are marketed and deployed without regulatory oversight, independent validation, or post-market surveillance. This article examines the current regulatory landscape for veterinary AI across the United States, European Union, and United Kingdom, and explores the roles of professional organizations in establishing standards through position statements and task forces. Liability and data privacy considerations are discussed, along with practical guidance for practitioners evaluating and adopting AI tools.
End of life care can seem chaotic with some owners wanting one thing and others focused on completely different things. When bad news happens or old age is starting to wear on a pet owner, it is important to have a conversation to establish goals of care for a pet at the end of its life. This helps everyone on the care team focus on the pet, maintaining quality of life while satisfying how the owner wishes to say goodbye to their pet. These goals also help reduce anxiety of the unknown surrounding death and help with the grieving process.
Veterinary hospice provides a compassionate, patient-centered approach to end-of-life care that emphasizes comfort, dignity, and quality of life over curative intent. This article outlines the principles of hospice care, including patient selection, multimodal pain and symptom management, environmental adaptations, and integration with geriatric care. It highlights the importance of proactive communication, quality-of-life assessment tools, and euthanasia planning to guide families through difficult decisions. By supporting both patients and caregivers, veterinary hospice strengthens the human-animal bond and ensures that a pet's final stage of life is managed with empathy, evidence-based strategies, and respect for individual values.
This article reviews computer systems that support veterinary clinical practice using artificial intelligence language models for language interpretation and generation, such as systems for client communication, medical records, clinical decision support, and clinical practice assessment. It provides guidance on incorporating tools based on large language models into clinical workflows to improve efficiency, clinical accuracy, and provider performance. Key inherent risks and recommendations for the responsible use of this technology by veterinary professionals are provided.
This article emphasizes the importance of strategic gastrointestinal biopsy collection, handling, and interpretation in dogs and cats. It highlights the need for high-quality, well-oriented samples, proper fixation, and detailed submission forms to ensure accurate diagnosis of inflammatory, neoplastic, and infectious diseases. The review discusses techniques for endoscopic biopsies, histopathologic evaluation, and ancillary testing, including immunohistochemistry and special stains. Proper communication among clinicians, histotechnologists, and pathologists is crucial for reliable results. Overall, optimized biopsy procedures and collaboration are essential for precise diagnosis, guiding effective treatment and improving patient outcomes in veterinary gastrointestinal medicine.
Veterinary geriatrics is evolving from an age-based label to a dynamic, functional assessment of how pets experience aging. This article explores key concepts including frailty, caregiver perception, functional versus medical aging, quality of life, and prediction of decline. Drawing from emerging veterinary literature and adapted human geriatric models, it offers practical tools and frameworks to support proactive, compassionate care for aging pets.
The term "triaditis" represents concurrent inflammatory diseases of the pancreas, hepatobiliary system, and small intestines in cats. This combination of inflammatory disorders appears to be clinically prevalent, and their co-existence may influence diagnostic or therapeutic strategies. The exact origin of the term triaditis in veterinary medicine is undetermined, but some of the earliest references to its potential existence were noted in the 1980s and 1990s. Despite the long-standing recognition of concurrent digestive-associated inflammatory disease in cats ('triaditis'), it is unknown whether this reflects a causative or associative relationship.
Protein-losing enteropathy in dogs is a complex syndrome characterized by excessive intestinal protein loss, most commonly resulting from intestinal lymphangiectasia and chronic intestinal inflammation. While glucocorticoid therapy is beneficial in many dogs, evidence increasingly shows that diet optimization, rather than aggressive immunosuppression, offers the best outcomes. The condition requires individualized management based on breed predisposition, diagnostic information, and patient response, with many steroid and immunosuppressive refractory cases responding to tailored dietary interventions.