The article explores the challenges of variability in veterinary radiology interpretations and how integrating AI in veterinary diagnostics can improve consistency, accuracy, and efficiency in diagnostic imaging. It highlights the role of AI as a supportive image screening tool that complements expert veterinary radiologists. In this article we’ll cover how:

• Radiograph interpretation can vary based on bias, experience, image quality, and clinical context.
• The use of AI in veterinary radiology can reduce interpretation inconsistencies.
• AI reports support—rather than replace—veterinary experts, helping boost accuracy and consistency in diagnostic imaging

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.

When you take a radiograph to better understand a patient’s condition, an accurate reading of the image is paramount to ensure the animal receives the appropriate treatment. That’s why U.S. board-certified radiologists on the Vetology team worked in conjunction with the technology crew to hone our artificial intelligence (AI) models. By integrating expert oversight, rigorous testing, and quality assurance measures, AI can support diagnostic efficiency while maintaining the trust and reliability veterinarians need for patient care.

To help you better understand the Vetology AI radiology tool, this article explains how it was developed and validated, and how it is improved.

​To develop our AI model, we used more than a million images from hundreds of thousands of cases, ensuring a comprehensive representation of anatomical variations and disease conditions. Each image was evaluated and annotated by a U.S. board-certified veterinary radiologist, providing high-quality, expert-labeled data (i.e., ground truth) that allows the AI to learn from professional interpretations.

CNNs are designed for image recognition and pattern detection, enabling the automated analysis of radiographs with high accuracy. The images first undergo preprocessing to ensure consistency. This includes image orientation, maximizing image clarity, and contrast adjustments. The CNN then learns to identify features and detect patterns.

• The first convolutional layers identify edges, textures, and contrasts, distinguishing bones, organs, and soft tissues.
• Multi-output CNNs can determine whether an X-ray belongs to a dog or cat and pinpoint the anatomical region being analyzed.
• Once trained, a CNN can determine orientation and recognize certain abnormalities and conditions.

A confusion matrix helps measure how well an AI model classifies radiographic images, ensuring it can correctly identify normal versus abnormal scans, specific conditions, and disease severity. It compares the AI’s predictions with the ground truth, which is determined by U.S. board-certified veterinary radiologists. The table below outlines the relationship between the four key components:

When used to evaluate results, the confusion matrix describes the AI’s performance by measuring key performance metrics, including:

• Accuracy = (TP + TN) / total cases
• Sensitivity = TP / (TP + FN) — How well the AI detects conditions
• Specificity = TN / (TN + FN) — How well the AI identifies normal cases
• Precision = TP / (TP + FP) — How many positive predictions are correct
• F1 score = The balance between precision and recall, ensuring AI does not over or under diagnose

QA regression testing compares AI-generated results with known labeled images to identify errors, inconsistencies, and areas for improvement. This process enables our developers to fine-tune the AI, reducing false positives and false negatives, and thus enhancing results over time.

Large Language Models (LLMs) are trained to recognize and generate common veterinary diagnostic phrases, sentence structures, and condition descriptions to create professional and structured reports.

Board-certified veterinary radiologists are once again involved to review the generated phrases and confirm that the AI is accurately interpreting the images and the LLM is producing relevant and coherent statements.

AI screening features enhance veterinary radiology through efficiency tools that promote improved AI reports and consistent image interpretation. Key features include:

• Image preprocessing and standardization: Pre-AI tools adjust orientation, brightness, and contrast for clearer analysis.
• Automated cropping: EfficientDet SSD technology isolates the area of concern, improving contextual accuracy for AI interpretations.
• Anomaly detection: AI identifies abnormalities, such as fractures, tumors, and changes in lung patterns, and can detect species- and region-specific changes. Severity grading models can also help classify the condition’s severity.

To ensure our AI model remains accurate and aligned with evolving veterinary radiology practices, we regularly update it with new data to integrate the latest medical findings and maintain optimal performance. The modifications undergo a structured change management process to ensure the updates improve accuracy without introducing errors, and we track all changes between AI versions to maintain transparency and traceability of updates.

Vetology’s AI radiology model is designed to support, not replace veterinary expertise, improving image analysis and providing clinicians with faster, more consistent insights. Utilizing an AI radiology tool can help veterinary teams make more informed decisions before seeking expert consultation. Veterinarians can use this tool as an initial screening step before sending cases to a teleradiologist, helping streamline workflows, prioritize urgent cases, and improve diagnostic efficiency.

The article highlights the role of AI as a supportive tool in veterinary radiology that enhances imaging workflows, accelerates decision-making, and strengthens collaboration between veterinarians and technology. Successful integration relies on thoughtful use, quality imaging, and continued learning. In this blog you will learn how:

• AI in veterinary radiology offers fast, consistent image screening to support confident treatment planning.
• Tools like Vetology’s AI report act as a guide—not a replacement—for veterinary or radiologist expertise.
• Embracing AI calls for some training, some collaboration, and an openness to evolving workflows.
• Vetology’s position is that Veterinarians, radiologists and developers should work together to ensure the AI tools we are building meet today’s clinical needs so they can solve the new challenges the future will bring

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.