A survey of 673 veterinary workers published in the Journal of the American Veterinary Medical Association in January 2026 found that 90.5% reported no or minimal formal AI training — yet 72.3% believe AI will alter veterinary medicine in significant ways. That gap between awareness and preparation is exactly where practices get burned. Diagnostic AI — tools that analyze X-rays, read blood smears, flag urinalysis abnormalities, and cross-reference imaging with lab data — is moving into the same territory that scribes occupied two years ago. But the implementation path is fundamentally different, the infrastructure requirements are heavier, and the stakes of getting it wrong are higher. If scribes were the easy on-ramp, diagnostics AI is the highway.

Diagnostics AI Is Not One Thing

Before your practice evaluates any diagnostic AI tool, it helps to understand what the category actually covers — because vendors use the umbrella term loosely, and the underlying technologies are quite different.

Radiology AI analyzes digital imaging files — X-rays, CT scans, MRIs — to flag abnormalities like fractures, masses, organ enlargement, or fluid accumulation. These tools work with DICOM files (the standard format for medical imaging) and typically generate structured reports or annotated overlays. SignalPET, for instance, runs every submitted case through its AI layer to screen for 63+ critical pathologies across thoracic, abdominal, musculoskeletal, soft tissue, and dental regions — with results delivered in under five minutes — and layers in board-certified radiologist access within the same platform. Vetology's approach publishes performance metrics publicly; its current validation dashboard covers 89+ classifiers across canine and feline imaging, with sensitivity, specificity, AUC, and radiologist agreement rate all reported per condition.

Pathology AI works at the cellular level — automated blood smear differentials, urine sediment analysis, cytology review. Tools like Zoetis Vetscan Imagyst and IDEXX SediVue Dx are already in many practices. A 2025 review published in Veterinary Clinical Pathology characterized this category accurately: AI augments the clinical pathologist; it does not replace them. The review specifically flags challenges like model drift, missing rare or novel diseases, and interlaboratory variation — all real operational risks that vendors don't typically highlight in their pitch decks.

Multimodal AI is the emerging layer where things get genuinely interesting (and complicated). This is where imaging data gets combined with CBC results, chemistry panels, signalment (species, breed, age), and clinical history to generate diagnostic probabilities. Academic models have shown strong early results — one model predicting feline infectious peritonitis using signalment and lab data achieved 95.45% sensitivity and 98.28% specificity against histology confirmation. But most of these models exist in research settings, not commercial products. The gap between a promising academic model and a validated clinical tool is wider than most vendor marketing implies.

Why Scribes Came First

The reason AI scribes spread through veterinary practices before diagnostic AI is structural, not arbitrary. Scribes require almost no technical infrastructure — a microphone, a software integration, and a SOAP note workflow. Diagnostic AI requires DICOM-compatible imaging equipment, connectivity between your imaging hardware and the AI platform, trained datasets specific to veterinary species and anatomy, and — critically — a radiologist validation layer that adds real cost and real latency to the pipeline. The technical barrier is real, and practices that underestimated it have learned the hard way.

The Second Opinion Problem

The JAVMA 2026 study found that 85.2% of veterinary workers do not believe AI will completely replace radiologists. That consensus is probably right — but it's also a distraction from the more important question. The actual tension in diagnostics AI isn't replacement versus non-replacement. It's whether these tools function as a genuine quality improvement or just a faster version of the workflow you already have.

A radiology AI that flags cases at high speed is valuable only if its flags are calibrated to your patient population. If a tool was trained predominantly on a dataset of large urban referral hospital cases and your practice is a mixed rural clinic, its false negative rate on your most common presentations may be substantially higher than the vendor's published accuracy figure. Most vendors publish aggregate performance data. What you need is performance data on cases similar to yours — by species, breed distribution, body condition, and condition prevalence.

The Diagnostic Error Question

The Academy of Veterinary Technicians in Diagnostic Imaging has noted that overreliance risk is a genuine concern — the tendency for clinicians to anchor on what the AI flags and mentally discount what it doesn't. An AI that produces a structured report with a clean negative finding can actually increase diagnostic confidence beyond what the data warrants. That's not a vendor flaw. It's a human factors problem that practices need to manage deliberately, with protocols that treat AI output as input to clinical judgment rather than a conclusion.

The practical frame for evaluating any diagnostics AI tool: does this reduce diagnostic errors in my practice, or does it speed up the same errors I'm already making? That question doesn't have a vendor-supplied answer. It requires you to track outcomes, flag misses, and build an internal performance record over time.

Vetology expands AI validation dashboard — March 31, 2026
Vetology expanded its publicly available performance dashboard from 4 metrics to 11 metrics per classifier, now covering 89+ validated classifiers across canine and feline thoracic, abdominal, and musculoskeletal imaging. Reported metrics include sensitivity, specificity, PPV, NPV, AUC, F1 score, accuracy, prevalence, confidence intervals, and Radiologist Agreement Rate. Worth noting: a 2026 Frontiers in Veterinary Science audit cited in the same release found that 63.3% of commercial veterinary AI vendors disclose no validation data publicly. (PR Newswire, April 1, 2026)

SignalPET launches 360° platform
SignalPET's 360° platform integrates AI radiology reports (covering 63+ pathologies, results under 5 minutes), PACS, and 24/7 board-certified radiologist access into a single system. The company reports more than 2,500 active clinics and 7,000+ active clinicians worldwide. The platform is designed to run on every case automatically — the AI layer acts as a first pass, with radiologist escalation available for any case requiring a signed interpretation. (SignalPET)

Veterinary AI diagnostics market hits $1.94 billion
The AI-powered veterinary diagnostics market reached $1.94 billion in 2025, up from $1.61 billion in 2024, and is projected to reach $4.05 billion by 2029 at a 20.2% CAGR. North America holds the largest regional share. Separately, Straits Research pegs North America's share at 55.44% of global market in 2025. This is a category with serious capital behind it — which means more tools entering the market faster, with varying levels of clinical validation. (Research and Markets / GlobeNewsWire, November 2025; Straits Research)

JAVMA study: vet workers are AI-curious but undertrained
A January 2026 JAVMA study (673 respondents, US and Canada) found that 90.5% of veterinary workers report no or minimal formal AI training, 56.6% believe AI will improve veterinary radiology, and 85.2% do not believe AI will fully replace radiologists. Only 25% reported AI was already in use at their workplace. The study covered veterinarians, technicians, specialists, residents, and students — the cross-section is broad enough to be credible as a baseline. (JAVMA / PubMed, January 30, 2026)

Here's my honest take: if your practice is still onboarding an AI scribe, or you're less than six months into using one consistently, diagnostics AI is not your next move. The practices that bolt on imaging AI before they've solved documentation are solving the wrong problem first. Get the scribe working, get your team comfortable with AI-assisted output, and build the internal discipline of reviewing AI-generated text before you sign off on it. That same discipline — treating AI output as draft, not final — is exactly what you'll need for diagnostics AI, but the stakes are higher and the workflow is more complex. For practices that are 6+ months into scribe adoption and looking for the next layer, this is where the ROI conversation starts to get genuinely interesting. Diagnostic accuracy, reduced radiologist consult costs, faster triage for critical cases — these are real numbers, if you pick the right tool and validate it against your own patient population.

The next time a diagnostics AI vendor reaches out — or you're evaluating one — ask this specific question: "Can you share the details of your validation dataset? How many images, what species and breed distribution, what conditions, and was the validation done independently or self-reported?" Most vendors won't bring this up unless you ask. It's the diagnostic equivalent of asking a scribe vendor for their accuracy methodology — and the answer tells you a lot about how seriously the company thinks about clinical reliability versus sales volume. A vendor with a real answer is worth more of your time.

Issue 7 covers front desk AI and client communication automation — the category that doesn't get the conference panel attention, but that every practice manager I talk to is quietly dealing with on a daily basis. It's a bigger operational lever than most practices realize.

No vendor affiliation. No sponsored content. Independent.