Industry News - Veterinary Teleradiology

Antitrust, Compliance, Industry, Industry News, Kickbacks, Tying and Bundling

Veterinary Equipment Bundling: Nobody Can Compete With Free

Antitrust, Compliance, Industry, Industry News, Kickbacks, Tying and Bundling / Editorial Staff

“A veterinary clinic is offered a CT scanner at no upfront cost. An ultrasound machine, a digital x-ray system, a PACS — all of it, free, or nearly free. The condition: sign a multi-year laboratory-services contract. The equipment is not free. Its cost is buried inside the lab contract, paid over years, and ultimately funded by pet owners through diagnostic pricing that never had to compete. This article is written from the perspective of the company that sells imaging equipment honestly, at a real price — and cannot survive against a price that is not real.”

Compliance, Education, Fee Splitting, Industry, Industry Basics, Industry News, Kickbacks, Licensing

Can’t Compete, So The Cheat?

Compliance, Education, Fee Splitting, Industry, Industry Basics, Industry News, Kickbacks, Licensing / Editorial Staff

“Human teleradiology operates inside a dense, actively enforced legal structure: the interpreting radiologist must hold a license where the patient sits, and federal and state law forbid fee-splitting, kickbacks, self-referral, and the tying of diagnostic services to other purchases. Veterinary teleradiology is governed by the same underlying legal principles — and a corporate aggregator model that routinely crosses them. This article documents the rules, the lines, the danger, the enforcement pathways, and the penalties. You decide what to call it.”

Industry News, Education, Industry, Industry Basics

The Monitoring Infrastructure Human Radiology Built

Industry News, Education, Industry, Industry Basics / Editorial Staff

“In May 2026, the American College of Radiology launched Assess-AI, which it describes as the world’s first AI quality registry for medical imaging, alongside a formal ACR-SIIM Practice Parameter for Imaging AI. The registry monitors the real-world performance of clinical imaging AI after deployment — detecting the performance drift and divergence-from-marketing that the human-side profession has formally acknowledged is routine. The United Kingdom’s Royal College of Radiologists has built parallel monitoring infrastructure. The human-medicine professional bodies recognized the problem and built the cure. The veterinary profession’s own specialty bodies issued a near-identical diagnosis in their 2025 position statement on veterinary AI — and built nothing.”

Compliance, Fee Splitting, Industry, Industry News, Kickbacks, Tying and Bundling

Veterinary Teleradiology Kickbacks: The Law & The Industry

Compliance, Fee Splitting, Industry, Industry News, Kickbacks, Tying and Bundling / Editorial Staff

“The federal Anti-Kickback Statute does not reach the veterinary side, but Nevada NRS 638.1404 prohibits referral compensation arrangements not disclosed to the client, Texas categorically prohibits them regardless of disclosure, and the AVMA Principles of Veterinary Medical Ethics state that “a veterinarian should not offer or receive any financial incentive solely for the referral of a patient.” Despite this legal architecture, the operational forms the prohibited conduct takes — published loyalty programs like IDEXX Points where points scale with referral volume, private per-study referral compensation characterized as marketing fees, equipment-placement deals tied to referral commitments, and corporate-consolidation revenue capture under vertically integrated structures like Mars Petcare’s Antech-AIS-VCA-Banfield architecture — remain a routine feature of the commercial market. This article documents the legal framework, the operational forms the prohibited conduct takes, the harm to patient care and client trust, and what happens when state attorneys general, state veterinary boards, or federal enforcers begin to take notice.”

Compliance, Industry News

Vet AI Position Statement: 18 Months of Institutional Silence

Compliance, Industry News / Editorial Staff

“In March 2025, the American College of Veterinary Radiology and the European College of Veterinary Diagnostic Imaging published a joint position statement in JAVMA establishing that commercial veterinary AI radiology products do not currently meet the standards required for safe deployment in clinical practice. The position statement was the formal, peer-reviewed expression of the field’s specialty college finding that an entire commercial product category fails the threshold for clinical use. In the eighteen months since, the institutions positioned to act on the position statement’s findings have not done so. The ACVR has continued to host the same AI vendors as official conference partners. The AVMA has issued no policy resolution and modified no corporate-relationship framework. AAHA, the only voluntary accrediting body for companion-animal veterinary hospitals in the United States and Canada, has completed the first comprehensive Standards of Accreditation refresh in its 90-year history without adding any standard that would constrain commercial AI radiology products. The institutional inaction is consistent across all three institutions, occurring in the same eighteen-month window, with the same documented professional notice, and with the same documented corporate sponsorship architecture connecting each institution to the corporate parents of the AI vendors at issue. This article documents what was said, what was not done, and why the structural pattern of inaction is explicable by examining how veterinary professional self-regulation is funded and organized.”

Industry News, Industry Basics, Tying and Bundling

Veterinary AI’s Training-Set Problem — Part Three: The Validation Statistics

Industry News, Industry Basics, Tying and Bundling / Editorial Staff

“The first two parts of this investigation calculated the labor required to produce the training corpora claimed by SignalPET, Vetology, and Antech RapidRead, and demonstrated that the math does not work — at the simplest annotation step, at the bounding-box step, at the segmentation step, and against the structural infrastructure veterinary medicine has not built. This article closes the series by addressing what happens after training is supposedly complete: what the products are required to demonstrate, what they actually demonstrate, and the corporate revenue model that explains why a category of medical-decision-support software exists that operates entirely outside the validation framework that constrains its human-medicine equivalent. The two halves of this article are different in tone — the first half is technical and statistical, the second half is structural and economic — but they answer the same question: why is the foundational accuracy claim of commercial veterinary AI radiology software so consistently weak, and so consistently absent from the kind of independent verification the human-side AI category requires as a precondition of going to market?”

Industry News, Industry Basics

Veterinary AI’s Training-Set Problem — Part Two: The Bounding-Box Step

Industry News, Industry Basics / Editorial Staff

“Part One of this investigation calculated the labor required to apply image-level categorical labels to the training corpora claimed by SignalPET, Vetology, and Antech RapidRead at the Stanford CheXNeXt rate of 34.3 seconds per image — the simplest possible AI training task. The math at that simplest step did not work for the larger claims. This article applies the published bounding-box and pixel-segmentation rates from the human medical imaging literature to the same vendor claims, and adds three structural infrastructure questions Part One did not address: the absence of subspecialty fellowship training in veterinary radiology, the scarcity of pathology-confirmed ground-truth datasets, and the breed-specific anatomic variation that prevents direct application of human chest x-ray training methodology to veterinary subjects. The conclusion: the foundational claim is not just unlikely. It is structurally impossible at the scales the marketing presents. The math, the workforce, and the upstream data infrastructure all point to the same conclusion.”

Industry, Education, Industry Basics, Industry News

Veterinary AI’s Training-Set Problem — Part One: The Labeling Step

Industry, Education, Industry Basics, Industry News / Editorial Staff

SignalPET claims its AI was trained on “over 2 million annotated veterinary radiographs.” Vetology claims “over 300,000 Board Certified veterinary radiologist-reviewed cases.” Antech RapidRead claims “16 million images.” This is Part One of a two-part investigation into whether those numbers can be reconciled with the documented capacity of the North American board-certified veterinary radiologist workforce. This article focuses on the simplest possible AI training task — image-level categorical labeling, the kind the Stanford CheXNeXt study measured at 34.3 seconds per image in PLOS Medicine — and shows the math does not work for the larger claims even at this most charitable level.

Compliance, How to Choose a Provider, Industry News, Uncategorized

Veterinary AI Validation Lags Human Radiology by a Decade

Compliance, How to Choose a Provider, Industry News, Uncategorized / Editorial Staff

“A peer-reviewed Frontiers commentary published in June 2025 by four veterinary AI researchers — including the lead author of the ACVR/ECVDI’s official AI position statement — methodically dismantled the only published external validation study of a major veterinary AI radiology product. Circular ground truth, severe class imbalance, sensitivity of 0.444 in difficult cases, the wrong statistical test, no version traceability. That is the state of validation in commercial veterinary AI. On the human side, by contrast, a model called CheXNet was trained on 112,120 publicly released chest radiographs in 2017, validated against three independent cardiothoracic specialists, published in PLOS Medicine, and then beaten on the public leaderboard by hundreds of subsequent teams. That is what the scientific method looks like in medical AI. The veterinary industry skipped it.”

Compliance, Industry, Industry Basics, Industry News

Veterinary AI Radiology: The Regulatory Gap Vendors Exploit

Compliance, Industry, Industry Basics, Industry News / Editorial Staff

“In human medicine, an AI system is not allowed to issue a diagnostic radiology report to a referring clinician without a licensed physician in the loop. Three separate regulatory layers — FDA device clearance, state medical practice acts, and CMS reimbursement — reinforce each other to make that prohibition operational. In veterinary medicine, none of those three layers applies to AI reading of radiographs. Vendors including SignalPET’s SignalSTAT, Vetology’s Virtual AI Radiologist Report, and Antech’s RapidRead are selling AI-generated radiograph interpretations to referring general practitioners with no board-certified veterinary radiologist review — a practice the ACVR and ECVDI have formally stated no current commercial product meets the standard to perform.”