The Ethics of Veterinary AI: Trusting Your Teleradiology Platform

At Vetology, our interpretation of ethical AI is baked into the core of our products. Our team cares about patient wellness as much as our clients do. That’s why we are careful to position our tools as screening aids, not diagnostic replacements. Guiding clinicians in how to use this evolving technology responsibly is a critical part of our mission.

Ethical AI starts with how systems are trained, validated and deployed. While veterinary medicine does not have a HIPAA equivalent, our ethical responsibility is no less important than that of human medical professionals. In many ways, the lack of formal regulation makes it even more important for veterinary AI companies to build software with integrity and transparency.

The best veterinary AI should be transparent about its purpose, built on thoughtfully designed data, and always safeguard patient and client privacy, giving veterinarians confidence in every result. These principles guide our development at Vetology and support veterinarians in integrating AI responsibly to complement their treatment decisions.

Veterinary diagnostics have inherent challenges, with or without utilizing AI. Image quality, proper positioning, collimation, and capturing the right number of views all affect both a radiologist’s and a clinician’s ability to identify disease conditions. AI cannot overcome non-diagnostic images, and veterinary AI companies should be transparent when image quality limits their ability to interpret results.

Finally, ethical communication also means being realistic about capabilities. Technology should never promise more than it can deliver. Companies should not make broad claims such as “AI matches the full depth and scope of traditional radiology reports” or “AI can detect subtle differences that humans cannot.” Accuracy and realism help maintain trust and ensure AI is used effectively to support patient care.

Vetology’s AI and Language Models are carefully trained on species-specific veterinary phrasing and images, which allows us to flag findings and generate preliminary conclusions and recommendations. These outputs are designed to assist clinicians, but they do not replace human expertise. In fact, they rely on it: a radiologist or veterinarian is always required to interpret results within the full clinical context.

Our AI functions as a screening tool, it reviews images without access to the patient’s history, lab results, or signalment. Instead, it analyzes visual patterns to identify potential abnormalities and generates an initial report.

The distinction between a screening report and a diagnostic report may seem subtle, but it’s significant. Screening reports highlight potential abnormalities and speed up interpretation, allowing veterinarians (domain experts) to focus their time, prioritize additional diagnostics, and narrow their clinical differentials.

Ethical AI development also requires responsible handling of training data. While a dog won’t mind if a computer learns about comparative heart sizes from its X-rays, its owner’s data deserves protection.

Vetology’s AI was trained on more than 15 years of veterinary radiology reports from more than 1,000 clinics and 20 board-certified veterinary radiologists. This dataset reflects real-world veterinary cases across species, breeds, and radiographic variations; equally important is how the data is collected.

Before training a new condition classifier, client and patient data are anonymized. Identifying details are removed, while essential information such as a pet’s (first) name, signalment, and history may be retained for clinical context. Vetology’s images and data are used solely for AI training and never shared beyond that purpose.

Vetology’s radiology AI screening tool is not a generative model; this distinction is important. Generative AI can introduce “hallucinations,” or fabricated yet seemingly accurate interpretations, which can be dangerous in a medical context. Instead, Vetology’s system analyzes X-rays and generates screening reports using pre-defined veterinary medical terminology.

This supervised training approach is critical for medical AI. This means clinicians can lean on the AI’s flags and recommendations, while still relying on their own expertise and patient context to make final diagnostic and treatment decisions.

In veterinary imaging, accuracy, reproducibility, and patient safety must come first. Our approach prioritizes these principles to enhance clinical decision-making while minimizing risk.

AI works best when it amplifies human expertise rather than trying to replace it. Vetology’s screening reports work with the clinician, because the veterinarian is the final decision-maker.

By integrating AI into their workflow, veterinary teams can streamline interpretation, manage caseloads more efficiently, and reduce cognitive load, all while ensuring patients receive the highest standard of care. It’s important to highlight the role veterinarians themselves play in the ethical use of AI in practice. By combining AI insights with clinical judgment, critical thinking, and diagnostic data, veterinarians can ensure that their use of AI innovations prioritizes the well-being of their patients, clients, and the professionals delivering care, while integrating AI tools safely and responsibly.

Veterinary radiology is a common diagnostic tool used to evaluate conditions ranging from orthopedic injuries to internal diseases. However, the reality is that radiologists don’t always agree on an image’s interpretation. Unlike laboratory tests with definitive results, radiology reports are clinical opinions, influenced by individual expertise, experience, and subtle differences in image quality. This variability in diagnosis can lead to differences in treatment recommendations and patient outcomes.

In this article, we look at the factors that influence these discrepancies and how advancements in artificial intelligence (AI)-assisted veterinary radiology can help improve consistency in diagnostic imaging reporting.

Radiology combines art and science, and differences in image interpretation are common, even among board-certified radiologists. Radiology reports rely on expert opinion, which can vary based on several factors:

• Subjectivity and cognitive bias — Radiologists rely on pattern recognition to identify abnormalities, and subtle differences in perception and cognitive bias can lead to different conclusions. For example, confirmation bias may make the radiologist see what aligns with their expectations, and anchoring bias can make them stick to an initial assessment.
• Experience — A radiologist’s experience can influence their interpretative skills and diagnostic approach, and their background can shape how they assess an image. For instance, specialists in orthopedic imaging may emphasize bone structure, while those with soft tissue expertise may focus more on organ abnormalities.
• Image quality — Underexposed or overexposed images can obscure fine details, and poor patient positioning may affect visibility, leading to misinterpretation.
• Clinical context — Veterinary radiologists are trained to interpret images without first looking at the patient’s history to keep their assessment objective. That said, a strong clinical history that includes exam findings and relevant background helps shape a more complete and accurate report. The more context a radiologist has, the better they can tailor conclusions and recommendations. In some cases, the same images might lead to different interpretations depending on the clinical details provided.
• Complex cases — Some conditions, such as early-stage tumors, inconspicuous fractures, or certain lung diseases, can present with subtle or overlapping features, making classification difficult. Differences in how radiologists weigh the significance of these findings can lead to varying interpretations.
• The human factor — Radiologists are humans, and issues such as fatigue and time constraints can impact diagnostic accuracy. Evaluating hundreds of images per day can also impact a radiologist’s mental focus, and a heavy workload may lead to less thorough evaluations.

When developing our veterinary AI radiology tool, the Vetology team set out to understand where radiologists consistently agreed—and where their interpretations differed. Identifying conditions with high agreement rates between different radiologists guides our selection criteria for building new AI classifiers. Studying patterns of diagnostic variability helps train the models to better handle ambiguous cases.

This process isn’t static. Our models continue to evolve through regular retraining, and input from real-world clinical use. Feedback from veterinarians and our internal human case reviews play a key role in flagging areas where the AI might need more structure or refinement. It’s all part of our goal to ensure the AI aligns with expert-level thinking and delivers meaningful support.

To support our understanding of diagnostic consistency, the team asked veterinary radiologists—without any involvement from AI—to independently evaluate and diagnose images with a wide variety of canine conditions. The radiologists showed high levels of agreement with one another on conditions such as pregnancy, urinary stones, hepatomegaly, small intestinal obstruction, cardiomegaly, pericardial effusion, and esophageal enlargement. In other words, these diagnoses were more consistently interpreted across different experts.

In contrast, there was noticeably lower agreement among radiologists on conditions like pyloric gastric obstruction, right kidney size, subtle or suspicious nodules, and bronchiectasis, indicating that these findings tend to generate more varied interpretations even among experienced professionals.

AI has demonstrated significant potential in enhancing diagnostic processes and reducing variability when reading radiographs. For example, the Vetology team found that the radiologist agreement rate for canine hepatomegaly was 92%, while the Vetology AI tool had an 87.29% sensitivity and a 92.34% specificity. Third-party peer reviews also demonstrate the product’s value.

Researchers at Tufts University, Cummings School of Veterinary Medicine, performed a retrospective, diagnostic case-controlled study to evaluate the performance of Vetology AI’s algorithm in the detection of pleural effusion in canine thoracic radiographs. Sixty-one dogs were included in the study, and 41 of those dogs had confirmed pleural effusion. The AI algorithm determined the presence of pleural effusion with 88.7% accuracy, 90.2% sensitivity, and 81.8% specificity.

Researchers at the Animal Medical Center in New York, New York, performed a prospective, diagnostic accuracy study to evaluate the performance of Vetology AI’s algorithm in diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology-certified veterinary radiologist’s interpretation as the reference standard. Four hundred eighty-one cases were analyzed. The radiologist diagnosed 46 of the 481 dogs with cardiogenic pulmonary edema (CPE). The AI algorithm diagnosed 42 of the 46 cases as CPE positive and four of the 46 as CPE negative. When compared to the radiologist’s diagnosis, the AI algorithm had a 92.3% accuracy, 91.3% sensitivity, and 92.4% specificity.

While AI’s potential is clear, it is important to recognize that its implementation may call for a shift in how veterinarians approach imaging, read radiographs, and initiate their diagnostic pathways. This article explores why AI is important, how to use it effectively, and the collaborative effort needed to integrate this technology into practice.

Asking for help is a critical skill in a successful practice. Vetology’s Virtual AI Radiology Report is just that: a support tool, an answer sheet, a guide. It’s one of many tools in your medical toolkit, and it is essential to remember that it was never intended to replace veterinary expertise nor radiologists; it was built to complement both.

The effectiveness of AI depends on the quality of the submitted radiographs. Clear, well-positioned lateral and VD images that focus on the area of concern lead to more accurate reports. This underscores the importance of maintaining high imaging standards in clinical workflows.

As with any new tool, skillset, or appliance, adopting AI in veterinary medicine involves a learning curve, some change, and maybe some practice. AI is evolving and improving.  Developing effective tools requires close collaboration between veterinary professionals and developers. Input from veterinarians helps refine systems, ensuring they address real-world clinical needs. Academic peer-reviews support the integrity of the tool, and clinicians benefit from training, practice, and patience with these tools, understanding their capabilities and limitations.

Vetology views integrating with veterinary workflows as a collective effort. Our collective success depends on thoughtful implementation, high-quality radiographs, and collaboration. By working together, veterinarians, radiologists, and technologists can create tools that reinvent workflows that support patient care and maintain the highest standards of safety. This partnership is critical to ensuring that this new approach to imaging evolves as a trusted and valuable resource for the veterinary community.

We’re honored to be recognized among other best-in-class AI veterinary tools by Unite.AI!

At Vetology, our mission is to support veterinarians with innovative AI and teleradiology solutions that streamline diagnostic workflows and improve patient outcomes. Being featured alongside other leading technologies in the veterinary space reinforces our dedication to advancing veterinary medicine through AI-driven insights.

We’ve highlighted the section on Vetology below, but we encourage you to check out the full article to discover other exciting, emerging tools in the veterinary and AI space. The future might be many things, but it’s definitely not boring!

The veterinary field is undergoing a transformation through AI-powered tools that enhance everything from clinical documentation to cancer treatment. These innovative platforms are not just digitizing traditional processes – they are fundamentally reshaping how veterinary professionals approach patient care, diagnostic accuracy, and practice management. In this guide, we’ll explore some of the groundbreaking AI veterinary tools that demonstrate the incredible potential of artificial intelligence in animal healthcare, from smart collars that monitor vital signs to sophisticated oncology platforms that process billions of data points.

Vetology:

Vetology functions as an advanced AI diagnostic center where machine learning systems process veterinary imaging data to provide rapid clinical insights. The platform combines sophisticated image recognition technology with teleradiology services, transforming how veterinary practices approach diagnostic imaging while maintaining high accuracy standards through AI-human collaboration.

At its core, Vetology’s AI Virtual Radiologist engine processes radiographic images through multiple analytical layers. This system simultaneously evaluates anatomical structures, detects abnormalities, and generates detailed clinical reports within minutes. The platform integrates with existing practice management systems, enabling workflow integration while maintaining continuous synchronization with clinic records.

The system’s AI extends beyond basic image analysis, incorporating specialized algorithms for automated cardiac measurements and vertebral heart scoring. This technical foundation enables the platform to process multiple imaging modalities, achieving a 92% agreement rate with board-certified radiologists through its advanced pattern recognition capabilities. The platform also maintains a sophisticated teleradiology network, creating a hybrid system that combines AI efficiency with specialist expertise for complex cases.

Key Features:

• AI diagnostic engine with 5-minute report generation capabilities
• Automated cardiac measurement system with vertebral heart scoring
• Multi-modality processing framework supporting radiographs, CT, and MRI
• Integration architecture supporting major practice management systems
• Pattern recognition algorithms trained on extensive veterinary datasets