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How veterinarians use AI in pet care
Veterinary AI is not one kind of tool. Some systems help with paperwork; others generate suggestions or analyze clinical information. A tool’s availability—or a plausible-sounding answer—does not establish that it has been validated for a particular species, task or case.
Administrative work and consultation notes
AI may help automate administrative processes and reduce time spent on routine documentation. Scribing tools can transcribe and summarize a consultation. Their output is a draft, not a verified medical record: the veterinary professional needs to check it and correct errors or omissions before relying on it.
Decision support
A system may suggest possible explanations for signs, known as differential diagnoses, or help a clinician organize information. Those suggestions still need to be judged against the animal’s history, examination and other evidence. An unfiltered generated list should not simply be copied into a clinical record or treated as a diagnosis.
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Imaging and other analytic tasks
A 2025 review of AI in small-animal veterinary medicine describes applications including image analysis, early disease detection, administrative support and disease surveillance. These are areas of use, not proof that every product is mature, accurate or useful in routine care.
For diagnostic imaging and radiation oncology, a 2025 position statement from the American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) emphasizes keeping a veterinarian in the loop—preferably a board-certified radiologist or radiation oncologist to interpret AI outputs. It also calls for transparent reporting, expert participation, independent evaluation, peer-reviewed research and monitoring after implementation.
What AI cannot safely take over
The veterinarian remains responsible for decisions about treatment. UK professional guidance from the Royal College of Veterinary Surgeons (RCVS) says it is inappropriate to delegate clinical decision-making wholly to an AI tool. That is UK guidance for veterinary surgeons and registered veterinary nurses; professional rules differ by jurisdiction.
AI systems can produce biased or misleading information, including fabricated details. These are risks to assess, not a claim that every system fails in every case. Overreliance can also weaken clinical skills and professional judgment. The person using a tool needs enough subject knowledge to ask an appropriate question and critically assess the answer.
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- It cannot examine your pet. A generated suggestion does not replace a veterinary history, physical examination or other assessment needed for the case.
- It cannot decide whether an answer fits. A clinician must consider the tool’s sources, data quality, assumptions, limitations and the animal’s circumstances.
- It cannot make an unverified note trustworthy. AI-generated transcripts and summaries need manual checking and timely correction before they are used as clinical records.
- It cannot guarantee a diagnosis or outcome. Performance established for one model, dataset or task does not establish performance for a different product or real-world case.
Can AI tell you what is wrong with your pet?
A general-purpose chatbot or other AI tool may return possible explanations for symptoms, but its response is not a veterinary diagnosis. It may omit important possibilities, misread details or sound confident despite uncertainty. Do not use an AI answer to delay veterinary care or change a treatment plan; contact a veterinary professional for advice about your animal.
AI can be useful to help organize questions or information for a veterinary appointment, but it should not be treated as a substitute for that appointment. If a clinic uses AI to support care, the clinician—not the software—must decide what the output means for your pet.
What the available evidence does—and does not—show
Evidence about veterinary AI is specific to the systems, tasks and settings studied. A 2026 preprint describing PetQA tested language and vision-language models on a Korean benchmark of dog- and cat-related veterinary questions. The evaluated models performed worse on image-based questions than on text-only questions; retrieval-augmented generation and supervised fine-tuning produced inconsistent improvements. Those findings apply to the benchmark and models tested. They do not establish how every commercial veterinary tool performs in practice.
The reviewed evidence does not establish a general clinical accuracy rate, a population-wide rate of AI adoption in veterinary practice or a universal improvement in outcomes for pets. A result from a benchmark should not be turned into a claim about all clinics, products or animals.
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How a veterinary practice can assess an AI tool
There is no product-by-product comparison here, so no vendor can be ranked on this evidence. A practice evaluating a system can ask:
- What task is it meant to perform? Define the intended use and scope rather than assuming a tool is suitable for every clinical or administrative task.
- What evidence applies? Look for validation relevant to the intended species, setting and task, as well as known limitations. Distinguish independent evaluation and peer-reviewed evidence from a product’s own claims.
- Who reviews the output? Set out which professional checks results, when specialist interpretation is needed, and how errors or uncertainty are handled.
- What happens to data? Check where client and animal information is stored, whether it is used to train or improve the system, whether the developer can access or edit practice data, and what consent is needed.
- Does it fit the workflow? Consider whether staff can use it appropriately and whether generated records can be checked and corrected without creating unsafe gaps or extra work.
- How will it be monitored? Plan to review performance and problems after deployment, not just at purchase.
A 2024 implementation framework likewise describes safe deployment as involving defined objectives, sound data, training, workflow integration, consideration of ethical and legal obligations, and ongoing monitoring. A framework can guide implementation; following its elements is not, by itself, a guarantee that a particular tool is clinically safe.
Privacy questions for pet owners and clinics
AI tools may process information from consultations or practice records. RCVS guidance for the UK advises practices to consider the tool’s scope and limitations, data storage and privacy, how it was trained, whether it learns from live client data, whether clients consented to that use, and whether the developer can access or edit practice data. It also advises against disclosing client and animal data without consent or another justifiable reason, and recommends being open with clients about AI processing.
If you are unsure how a clinic uses AI, ask what information is processed, whether it is stored or reused for training, who can access it, and how the practice protects it. The applicable privacy and professional requirements depend on where you and the clinic are located.
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