AI in veterinary care is advancing, but clinicians still carry the risk

Artificial intelligence is gaining ground across healthcare, but the latest literature still frames it as an assistive technology rather than a replacement for clinicians. A recent narrative review of AI in healthcare concluded that tools in imaging, lab medicine, rehabilitation, and conversational systems can match or approach clinician-level performance on selected tasks in controlled environments, yet still face major barriers before broad clinical deployment, including weak generalizability, bias, ethical concerns, and limited prospective, real-world validation. (pmc.ncbi.nlm.nih.gov)

That same tension is now showing up in veterinary medicine. A systematic review in Animals found AI’s role in companion animal care is expanding beyond diagnostics into behavior assessment, welfare monitoring, and support for the human-animal bond, but the authors described adoption as fragmented and insufficiently integrated into everyday practice. Broader reviews in animal and veterinary science point to similar momentum in imaging, epidemiology, behavior monitoring, and precision care, while stressing that implementation still lags behind technical promise. (mdpi.com)

On the ground, commercial veterinary software companies are already marketing AI as a workflow layer rather than a single feature. Digitail, for example, has positioned AI for a range of clinic tasks beyond SOAP-note transcription, reflecting a wider industry push toward intake automation, record summarization, client messaging, and scheduling support. That commercial momentum matters because it means many practices may encounter AI first through practice software, not through a standalone diagnostic product or a peer-reviewed clinical tool. (digitail.com)

Regulators are starting to respond. In a March 2025 white paper, the American Association of Veterinary State Boards said AI may improve efficiency and potentially reduce workload, but warned that licensees must understand the risks and limitations, maintain transparency, protect client data, and preserve the standard of patient care. The paper makes a key distinction: veterinary practice acts generally regulate how a tool is used, not the tool itself. It also says chatbots should not provide diagnoses or treatment plans, AI-generated records must be thoroughly reviewed, and responsibility for interpreting radiographs, cytology, and other findings remains with the veterinarian. (aavsb.org)

That stance lines up with the broader regulatory picture. In human medicine, the FDA maintains a list of AI-enabled medical devices that have met applicable premarket requirements, and the agency has also issued transparency principles for machine learning-enabled devices. But the AAVSB white paper notes that veterinary AI lacks standardized benchmarking, certified training datasets, and, at least as described in the document, premarket testing or approval pathways comparable to those familiar in other parts of healthcare. The result is a more ambiguous environment for veterinary teams evaluating new tools. (fda.gov)

There are also signs the profession is open to adoption, with caveats. A recent survey of ACVIM and ECVIM-CA members found respondents were generally optimistic, with most agreeing AI tools will improve veterinary medicine and become part of their careers. That optimism is important, but it sits alongside persistent concerns about automation bias, hallucinated summaries, data privacy, and whether a model trained on one patient population, breed mix, or use case will hold up in another. The AAVSB specifically flags breed and phenotype diversity as a veterinary-specific challenge that can undermine model performance if training data are too narrow. (academic.oup.com)

Why it matters: For veterinary professionals, this is less a story about whether AI is coming and more a story about governance, workflow design, and clinical accountability. The near-term value is likely to come from administrative and decision-support uses that save time without displacing judgment: documentation, record review, communication drafts, image triage, and pattern recognition. But if practices adopt these tools without understanding data provenance, limitations, security, and who is ultimately responsible for the output, they may create new legal, ethical, and quality-of-care risks. For pet parents, AI may improve responsiveness and efficiency. For clinicians, it raises the bar on oversight. (aavsb.org)

What to watch: The next phase will likely center on validation and accountability: more profession-specific guidance from boards and associations, more scrutiny of training data and real-world performance, and clearer expectations for informed consent when AI moves closer to diagnosis or treatment decisions. Watch, too, for whether veterinary AI begins to follow the human-health pattern toward transparency standards and post-market performance monitoring, especially as tools become more embedded in everyday clinical software. (aavsb.org)