7 Best AI Veterinary Radiology Platforms for Multi-Site Clinics (2026)

Buying radiology support for one clinic is a fairly simple decision. Buying it for forty clinics is a different job, because whatever you pick becomes the standard every site has to live with.

The questions change too. Can the practice you acquired last month plug in without swapping its X-ray machines? Does a Tuesday-afternoon case at your quietest site get the same review as one at the flagship? And can anyone at head office see imaging volume across the whole network without ringing each practice manager?

The Short Version

Groups end up shopping for two layers at once. The interpretation layer reads the images. The infrastructure layer stores them and moves them between sites. Very few vendors sell both.

SignalPET comes out on top here because it covers both layers. It runs AI screening on every case and backs it with board-certified reads and its own web PACS.

The real problem most groups are solving is variance between locations. A model that looks brilliant at one well-staffed hospital matters less than every site getting the same baseline review. One trend is worth flagging early, too. Enterprise imaging vendors increasingly host third-party AI models on their infrastructure, and that has become a common route for AI to reach large groups.

So we’ve sorted the seven platforms by layer. Scoring an AI reading engine against an enterprise archive on the same checklist doesn’t tell you much.

What Group Buyers Weigh Differently

Start with onboarding. Acquired practices rarely share equipment brands, so a vendor-neutral platform saves real money each time a new clinic joins.

Consistency comes next, and it’s harder to test. A vendor’s benchmark accuracy matters less than whether your slowest, most junior site gets the same review as your best one.

Shared access counts for more than any single viewer feature. A vet at one site should be able to pull up a study from another without emailing anyone.

Pricing deserves a spreadsheet. Per-study, per-examination and per-user models look similar in a demo and drift far apart by the time you reach fifty sites.

And decide early where the AI will live. Some groups want a model running from day one. Others standardize infrastructure first and bolt models on later.

Whatever you choose, judge each vendor on the layer it actually covers.

The Platforms

1. SignalPET

SignalPET is the only platform here that handles screening, specialist reads and image storage under one contract.

Every study gets its Immediate Report automatically. The AI checks for 63+ clinically relevant pathologies across thoracic, cardiac, abdominal, musculoskeletal and dental X-rays in dogs and cats. Results average under five minutes, quick enough that the patient is often still in the building.

The “every study” detail is what matters to a group. Nobody at a busy site has to remember to send a case out, and a quiet Sunday shift gets the same check as a full Monday team.

For cases that need more, there are two further tiers. The Complete Report reads the whole case, factoring in signalment, history, and presentation, and comes back within about 30 minutes. When the AI isn’t confident, the case escalates to a board-certified radiologist at no extra charge. The Radiologist Report is a signed specialist read, available around the clock, with STAT and routine options and turnaround of 1 to 12 hours. More than 40 radiologists support the service, and a DACVR diplomate runs it as Director of Radiology.

Getting a new site live is quick. SignalPET says it works with about 99% of X-ray machines and existing PACS setups, and setup usually takes under an hour. An acquired clinic keeps the gear it already owns. Sites without a PACS can use SignalPET’s.

The design treats AI as triage, running before and alongside specialist reads, with a radiologist stepping in when needed. More than 2,500 clinics use it. Its underlying engine, SignalRAY, was compared against board-certified radiologists in a 2025 peer-reviewed study in Frontiers in Veterinary Science.

2. Sectra

If you want proof an enterprise PACS can run a large veterinary network, Sectra has a well-documented example. Back in 2018, Evidensia Sweden signed up for Sectra’s cloud PACS across 54 hospitals and clinics. The deal replaced a patchwork of imaging systems with one service, paid per examination, at roughly 100,000 examinations a year.

Sectra doesn’t build veterinary AI itself. Its Amplifier Marketplace works like an app store for third-party imaging models, which suits a group that wants one archive now and AI later. Equipment connects over DICOM, so a mixed estate of modality brands doesn’t need proprietary connectors.

3. SK Telecom X Caliber

Fifteen seconds. That’s roughly how long X Caliber, SK Telecom’s cloud AI, takes to return a read on a dog or cat X-ray, and there’s no server to install on site.

Coverage is broader for dogs: seven musculoskeletal, ten chest and sixteen abdominal conditions. For cats, it reads five chest and seven abdominal conditions. Heart-size measurement is built in for both species.

The product has travelled. SK Telecom reported 450 Korean veterinary hospitals on it by late 2023, and more than 100 Australian clinics when it launched there in 2024. It also has distribution partners in Japan, Singapore, Indonesia, Canada and the US. At launch in 2022, the company put sensitivity at 84 to 97%, depending on which area was being read.

4. Telerad Tech VetSpa

Of the infrastructure products, VetSpa was the most clearly built around multiple locations from the start. Scheduling is centralized across modalities, sites and centers, so a regional manager can see one calendar instead of ten.

Telerad Tech says it scales from a small clinic with a single digital X-ray system up to busy multi-modality hospitals. You can license it as VetSpa Enterprise or run it as a cloud (SaaS) service. For networks built through acquisitions, the practical points are integration with third-party scheduling and practice management software, a vendor-neutral archive, and round-the-clock support.

5. Mach7 Technologies

Most groups that grew by acquisition end up with four imaging systems that don’t talk to each other. Mach7 is built for that mess. Its pitch is consolidation, and its veterinary page speaks mainly to teaching hospitals and referral centers.

The setup has three parts:

• Enterprise Imaging Platform & VNA: handles archive, workflow engine, worklist, lifecycle management and migration.
• eUnity: a zero-footprint diagnostic viewer that opens studies in any modern browser, with nothing to install at each site.
• Flamingo: sits above your existing PACS, archives and AI tools and routes studies between sites.

Anything DICOM- or HL7-conformant can connect, and studies from every location end up in a single vendor-neutral archive. One thing to raise with sales: Mach7 states its regulatory clearances cover human clinical use, so ask how veterinary deployments are handled.

6. AdvaHealth AdvaPACS

AdvaPACS has the plainest pricing of the lot. You pay a per-study software fee, plus AWS infrastructure passed through at cost. There are no per-user or per-device licence fees, no lock-in, and no maintenance or annual support charges.

For a group, that removes a familiar headache. Nobody argues over seat counts at clinics with very different headcounts, and since modality connections are unlimited, a site with several machines costs no more to connect than one with a single X-ray room. AdvaHealth showed AdvaPACS at RSNA 2025 as a cloud-native, AI-forward platform. Read that as infrastructure built to carry other vendors’ models.

7. IMAGE Information Systems

IMAGE Information Systems sells iQ-4CLOUD VET, a cloud PACS made for vets and marketed to multi-site teams by name. Most enterprise vendors don’t pitch that directly. Staff can reach images from any site on a PC, tablet or phone.

Because it doesn’t care which vendor or modality produced the image, a group with odd equipment across acquired sites can still run one archive. The company is headquartered in Rostock, Germany, with offices in the US and India.

Packages are priced by storage, from 10 GB up to 2 TB. Prices aren’t published, so do the maths carefully if your group runs a lot of CT.

A Word of Caution on AI Accuracy Claims

Vendor accuracy figures deserve a skeptical read, for two reasons.

The research base is still thin. A 2026 systematic survey in Frontiers in Veterinary Science found most veterinary AI studies stop at algorithm development, and few test models externally or prospectively in clinics. Where independent testing has happened, results were humbling. In March 2026, Murdoch University researchers published a pilot study in JAVMA. They ran 53 general-practice canine abdominal cases with confirmed diagnoses through six commercial AI services, SignalRAY and X Caliber among them. Missed diagnoses were common across all six, and the authors judged none ready to make clinical calls alone.

Second, accuracy and sensitivity are different measures. A model can score well overall on obvious findings and still catch fewer true positives than a board-certified radiologist would.

For a group, the figure to watch is variance. Steady, decent performance at every site will serve you better than a stronger model that half your clinics barely use. That unevenness also helps explain why AI projects rarely show ROI once finance goes looking. Treat AI screening as a triage layer ahead of the specialist, and keep the specialist.

Questions Group Buyers Ask

Q.1 What does an AI radiology platform do in a vet clinic?

It reads radiographs and returns structured findings, usually within minutes, flagging anything suspicious for the attending vet. Most vendors call it screening or triage, done before a specialist looks, so the clinician has something useful while the case is still open.

Q.2 Can AI replace a board-certified veterinary radiologist?

No, and reputable vendors don’t claim it can. Studies suggest AI can match or beat radiologists on clear-cut findings but tends to miss more true positives overall. What works in practice is AI screening every case for speed, with tricky or ambiguous studies sent on to a radiologist.

Q.3 What should we check before rolling one platform out network-wide?

Start with compatibility with the modality equipment at each site, and with how a new acquisition gets onboarded. Check that imaging volume across all locations shows up in one view. Then model pricing per study, per examination and per user side by side, because those diverge quickly at scale.

Q.4 Is a PACS the same as an AI radiology platform?

No. A PACS stores, shares and displays images, while an AI platform interprets them. Most groups need both, and they’re increasingly bought together now that enterprise imaging vendors host third-party AI on their own infrastructure.

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