AI at the Point of Care
AI in Veterinary Diagnostics · Part 1 of 6
Of all the things veterinary medicine asks of you, diagnostics is the part that has always felt most like home to me. There is something deeply satisfying about a good diagnostic workup — the way a careful history, a thorough exam, and the right tests can turn a vague clinical picture into something clear and actionable. It feels like solving a puzzle where the answer actually matters. Where getting it right changes what happens to the animal in front of you.
I have spent my career at the intersection of clinical practice and diagnostic pathology — first as a clinician, then as a board-certified veterinary pathologist with a focus on histopathology. And I can say, without hesitation, that we are living through one of the most exciting periods in the history of veterinary diagnostics. Not because the fundamentals have changed — they haven't — but because AI has reached a point where it can genuinely make those fundamentals more accessible, more consistent, and more powerful for every clinician who uses it.
That is what this series is about.
Human medicine got here first — and that's good news
The technology behind AI diagnostics in veterinary medicine is the same technology already reshaping human healthcare. AI tools have been helping human doctors flag urgent findings, surface missed diagnoses, and reduce cognitive overload for years. AI-assisted skin cancer screening, sepsis alerts in emergency rooms, radiology tools that flag abnormal chest X-rays before the radiologist opens the file — these are real, in active use, and backed by solid evidence.
None of those tools replaced the doctor. What they did was reduce the mental burden at the moments of highest pressure — exactly the conditions that describe a busy veterinary practice. The same AI capabilities are now being applied to veterinary medicine. The translation isn't automatic or simple, but the foundation is proven and the direction is clear.
Context from human medicine
AI diagnostic tools are already embedded in major human health systems — from sepsis prediction in ICUs to AI-assisted radiology flagging in community hospitals. Moving these tools into veterinary medicine doesn't require proving the technology works. It requires proving the veterinary-specific version is built correctly.
Where information gets lost — and where AI can help
A diagnosis is only as good as the information behind it. In practice, the most critical moment is often the one in the exam room — when a clinician is weighing a differential list, deciding what to sample, and choosing how to document what they're seeing. This is also where information is most easily lost.
Clinical pattern recognition is one of the most powerful tools any veterinarian has. But it gets tired. It varies with experience. And in a busy practice, even the best clinicians can miss something — not from lack of skill, but from the sheer volume of decisions being made in a short window of time.
AI-assisted decision support isn't trying to replicate that pattern recognition. The goal is more practical: act as a structured thinking aid — prompting consideration of differentials, flagging gaps in the clinical picture, and surfacing possibilities that might not be top of mind in the moment.
What decision support is not: An AI diagnostic tool is not a diagnosis. It doesn't examine the patient. It doesn't replace the clinician's judgment. What it can do is organize, prompt, and structure — reducing the chance that an important differential gets missed or that a sample gets submitted without enough context.
Built on existing AI — with veterinary expertise built in
Here is something worth understanding about how these tools actually work: the underlying AI already exists. Large language models can already generate differential lists. Image recognition tools can already identify features in a photograph. The raw capability isn't the hard part.
The hard part is making sure that capability is applied correctly in a veterinary context. Without species-specific logic, an AI differential tool will apply human or canine assumptions across the board. Without knowledge of how different diseases present differently across species, suggestions can be misleading. Without an understanding of what matters diagnostically in each tissue type, image-based tools can point a clinician in the wrong direction. Like the experienced veterinarian who has learned to stay humble about what they don't know — an AI operating without specialist oversight doesn't know what it doesn't know.
The tools being developed at Vetopathy are built on these existing AI capabilities — but shaped, constrained, and refined with input from board-certified veterinary pathologists. PathIQ by Vetopathy uses AI-driven differential generation, but its logic is grounded in species-specific diagnostic reasoning developed and continuously refined by DACVPs. The goal isn't to rebrand a generic AI tool. It's to make the output less likely to be wrong in the ways that matter most to veterinary clinicians.
That distinction — AI capability shaped by specialist oversight — is what separates a veterinary diagnostic tool from a general-purpose chatbot that happens to know the word "lymphoma."
Seeing what's there: AI and gross lesion documentation
A related problem that doesn't get enough attention is the quality of lesion documentation at submission. Pathologists depend on clinical context. A biopsy submitted with a photograph that shows lesion shape, surface character, and location gives a pathologist far more to work with than one submitted with "skin mass, left flank."
AI tools that analyze clinical photographs for morphologic features — surface texture, color change, shape, distribution — are being developed to help close this gap. The idea is to generate a more informed differential list at the point of submission, and to help the clinician build a better clinical narrative before the histopathology results come back.
PathView by Vetopathy is being developed in this space. Like PathIQ, it combines AI image analysis with species-specific logic reviewed by veterinary pathologists — because image recognition alone, without that clinical grounding, is not enough.
Why specialist oversight reduces risk: General AI models are trained on data that skews heavily toward human medicine and common domestic species. They produce confident-sounding output regardless of whether their training supports it in a given context. A framework built with veterinary pathologist input adds species-specific guardrails, adjusts differential weighting by prevalence, and catches the errors a generic model would never flag — because it doesn't know what it doesn't know.
Where this is headed
The near-term story for AI in point-of-care veterinary diagnostics isn't about dramatic leaps. It's about thoughtful integration — tools that fit inside existing workflows, that meet the clinician at the moment of decision, and that carry clinical reasoning forward rather than letting it disappear between the exam room and the laboratory.
There are real open questions. Veterinary-specific validation of AI tools is still limited. Species diversity creates complexity that human medicine tools don't face. And over-reliance on any decision-support tool — AI or otherwise — is a genuine risk worth naming.
None of that means the tools being built aren't valuable. It means they should be adopted thoughtfully, with a clear understanding of what they have and haven't been shown to do — and with preference for tools built with specialist oversight, not just impressive output.
Next in this series: AI and the diagnostic sample — how artificial intelligence is beginning to influence cytology interpretation, biopsy site selection, and what gets submitted in the first place.
Written by Eric Snook, DVM, PhD, DACVP · Vetopathy LLC