AI in Veterinary Medicine
How AI Is Shaping The Workplace:
In the last couple of years, AI hasn’t just been a gimmick in peoples spare time, it has taken over all industries by storm. It is less about replacing vets and more about changing how professionals spend their time and make decisions. In this blog post we are going to explore how different AI tools have assisted Veterinary Professionals to provide the high quality of care they love.
Understanding The Background:
Back in the 2000s the earliest ‘AI like’ tools were basic expert systems software that were designed to mimic clinical decision making using rules. These weren’t true modern AI but they were built on early ideas from Artificial Intelligence.
Programs that helped veterinary professionals work through diagnostic decision trees. For example if symptom A and B, consider disease X’
Herd management tools in livestock farming used data to guide feeding, breeding and disease control
These early systems were very limited, rigid and hard to update. They were dependent on manually coded knowledge which has been later replaced. In the mid 2000s to the mid 2010s, digital record keeping became a lot more common, veterinary practices then started generated usable datasets. This then allowed early machine learning to creep in.
Practice management software began automating scheduling, billing and reminders
Livestock farms adopted sensor based monitoring, for example, milk yield tracking and activity sensors for cows
Early predictive models appeared, especially in agriculture which were identifying disease outbreaks or productivity trends
At this point adoption in small animal clinics was relatively slow due to the cost and limited data available to implement. Looking at more modern adoption of AI we can see that it became genuinely useful in everyday veterinary workflows. Advances in computing over the last 20 years and access to cloud based tools made a huge difference in the accessibility of AI.
Diagnostic imaging tools began using deep learning to analyze X-rays and CT scans
AI assisted radiology platforms became available to general practices
Wearable pet tech started feeding health data into AI driven insights
Chatbots and triage tools appeared to assist with client communication
All of these things assisted in the rapid expansion of AI tools that we now see in veterinary medicine today. Especially after the digital acceleration caused by COVID-19 in 2020.
In the present day:
So how exactly are AI Tools assisting with providing high quality care in the Veterinary Practice today?
Well, a simple way to answer this would be to say in more ways than you’d expect. But lets start with the most common and adopted form, AI Scribes…
In the workplace the use of notes are crucial to a veterinary professional, be it the technicians or the doctor themselves. Traditionally, note-taking during appointments has been the responsibility of veterinary professionals. While essential, many staff felt it took valuable time and attention away from patient care.
The introduction of AI
Tools such as Covetrus and ScribbleVet have allowed note taking to be automated and out of the hands of the professional. This aids in productivity, time management, quality of care, less burnout and overall quality of the notes being taken as the scribe hears the entire conversation and does not miss a thing.
Now of course these systems are not perfect and they do come with their flaws and hiccups but the real benefit of AI is that it is constantly learning (which is quite scary in some aspects). But, and here’s a real emphasis on but, this means that the experience can only get better, more productive, further aiding the practice in carrying out its duties!
A quick guide to a few AI tools (that vets are using already):
AI Scribes:
AI scribes are the most loved AI tool that has been used in the workplace across many different professions for years. In the Veterinary space this can convert consultations into structured SOAP notes automatically, saving the Dr or the Assistant precious time note taking. Looking at the Merck Wellbeing Study almost 61% of vets are more exhausted than the general population and the cost of burnout is between $1-2 billion per year.
Yes I said BILLION. The burnout epidemic in the Veterinary world is a huge issue with Dr’s sacrificing their energy and their
personal lives for the work they love. UC Davis and the University of Florida wrote that using AI scribe can save nearly two hours of time a day. The real world evidence shows for every patient there is 5-15 minutes saved on average, this is precious time and energy in a world where burnout is such a prominent issue.
The evidence is not all perfect though. A pilot study of an ambient AI scribe in human palliative medicine found a genuinely mixed picture… One clinician saw a statistically significant reduction in documentation time, while a second saw essentially no improvement. This is a useful reminder that results depend heavily on the individual, the workflow and the specific tool. Not just the underlying technology.
Diagnostic Imaging:
Radiology is where AI in Vet Med has drawn the most scrutiny and the picture that emerges is genuinely mixed. Individual studies show real promise, for example…
A convolutional neural network trained to detect left atrial enlargement on thoracic radiographs, using a database of nearly 800 patient echocardiograms, classified images with just over 82% accuracy and 85% concordance with board certified Radiologists.
A separate algorithm that was built to catch technical errors in canine thoracic radiographs, rotation, under or overexposure, limb mispositioning had reached 75 to 82% accuracy depending on the view.
Another comparison showed that commercial AI software matched the best individual Veterinary Radiologist on overall accuracy however, it was more specific but less sensitive than human readers.
All of this sounds great, however these sorts of tools often have their downsides, for example..
A 2026 pilot study published in the Journal of the American Veterinary Medical Association tested six commercial Veterinary Radiology AI platforms against real general practice canine abdominal radiographs with confirmed diagnoses, rather than curated, idealized datasets.
The results were far less reassuring, accuracy ranged from 70-90% while balanced accuracy from just 60-65%.
The authors concluded that none of the six platforms was reliable enough for standalone clinical diagnostic use, given how often the missed genuine abnormalities.
So the message across the board is that AI imaging tools are best treated as a second set of eyes not a replacement for a Radiologist’s judgement. Vendors such as cetology frame their products this way explicitly, positioning AI as a way to flag likely findings for a human to confirm rather than issue a standalone diagnosis.
Client Communication and Triage Chatbots
After looking into how AI can help with the medical side of the Veterinary World, lets dive deeper into the front desk…
AI driven chatbots and virtual receptionists now deal with routine client questions, book appointments and send vaccination reminders, outside of office hours. Their job is to filter routine traffic so staff can focus on urgent or more complex cases.
Triage is actually the more clinically meaningful AI tool. Triage agents take in client messages and symptom descriptions and flag urgent cases for immediate staff attention, which is super important in emergency and after hours settings.
The evidence in the case of triage agents is still mostly early days and largely borrowed from human medicine.
GPT 4 has been shown to make emergency medicine triage decisions that align closely with established clinical standards and in veterinary specific research, a study using GPT 3.5 identified overweight body condition score cases in clinical test with high precision.
In 2024 a study introducing AI generated cases and AI simulated clients into veterinary communication training, found that students engaged well with the format, though the authors noted the profession as a whole is still cautious about AI adoption.
What does this mean for the profession?
Looking at the research, the result is more nuanced than either the hype or the skepticism around AI in Veterinary Medicine would suggest. Documentation tools, such as the AI Scribe, have some of the strongest an most consistent evidence behind them. This is largely due to the task they are given (turning speech into notes) is very much well suited to the current state of AI.
Diagnostic imaging tools do show a real promise where the study is in a controlled environment but in a real world messy work environment where not everything is clear and ‘as it should be’ the tool begins to show its cracks.
Client facing AI chatbots and triage tools are useful for filtering volume and taking a huge weight off of the front desk, allowing for fast and automated service. The only downside is that currently the evidence is still largely borrowed from human medicine and so this has its own path to go down before it is trusted in the veterinary world.
So all in all, how is AI actually shaping the workplace?
One task at a time and it is constantly growing and learning.
The tools that succeed are the ones that keep a human clearly in the lop and the practices getting the most value are the ones treating adoption as something that is ongoing and evaluated raters than a one off purchase. Astime goes on, there will be more independent studies and more evidence for clinics and professionals to go off. This will provide clarification on the best use cases for AI tools and where requires still a little bit of skepticism.