This article examines the comparison between using AI in veterinary radiology and the human experience. Even though AI does improve efficiency by pre-screening X-rays and generating reports, it cannot replace radiologists due to variability in interpretation. AI performs best in clear conditions with strong expert agreement, while complex cases still require human expertise. Read more about how AI in radiology:

• Addresses the shortage of veterinary radiologists.
• Helps with pre-screening and structured reports.
• Works well for conditions like hepatomegaly or pericardial effusion.
• Supports, not replaces, veterinary radiologists.

About 94 million U.S. households own at least one pet.[1] That’s a lot of furry, feathered, and scaly family members that may potentially need radiographs to diagnose a medical condition. However, there are only 667 board-certified radiologists in the country [2] creating a bottleneck in radiology services. This shortage can correlate to longer wait times, increased anxiety for clinicians and pet owners, and potential delays in diagnosing critical conditions.

This is where artificial intelligence-based radiology tools can help—not to replace veterinary radiologists, but to support them. Artificial intelligence (AI) can pre-screen images, highlight abnormalities, and generate structured reports, allowing radiologists to focus on complicated cases while improving efficiency for general practitioners. But, how does AI compare to human expertise?

Not all conditions are created equal

Radiology is not an exact science but rather an interpretive discipline that relies on pattern recognition, clinical judgement, and experience. Board-certified veterinary radiologists undergo extensive training, but they don’t always agree on image interpretations, especially if the changes are subtle or the patient has multiple diagnoses, creating overlapping signs.

Studies have shown that radiologists tend to have a high level of agreement when interpreting X-rays that display clear and advanced disease. However, variability in interpretation increases when findings are more subtle, as may be the case in early-stage tumors, mild joint changes, or diffuse lung patterns that could indicate interstitial or early inflammatory disease. When subtle abnormalities are suspected, additional imaging, such as ultrasound, computed tomography (CT), or magnetic resonance imaging (MRI), can provide greater anatomical detail and diagnostic confidence.

How interpretive variability affects AI performance assessment

Understanding variabilities in radiologist interpretations is necessary to fairly evaluate the AI’s diagnostic accuracy, sensitivity, and specificity.

• AI algorithms rely on human-labeled data (i.e., ground truth) to learn how to detect and classify abnormalities, and if radiologists don’t agree on a diagnosis, the ground truth may have some degree of subjectivity.
• AI radiology tools are evaluated using accuracy, sensitivity, and specificity, but these measures must be analyzed in the context of how consistently radiologists themselves diagnose the condition.
• If two radiologists interpret the same case differently, the AI may match one but disagree with the other. This doesn’t mean that the AI is wrong; it only highlights the inherent variability in radiology.

Vetology’s AI tools provide guidance for a wide range of thoracic, abdominal, and musculoskeletal conditions in canine and feline patients, including—but not limited to—the following

Thoracic classifiers

• Pulmonary patterns
• Cardiomegaly
• Pleural fissure lines
• Fluid accumulation
• Soft tissue pulmonary nodules
• Masses
• Vascular enlargement

Leveraging AI screening alongside teleradiology

Vetology allows veterinarians to optimize AI radiology screening tools and teleradiology services to enhance diagnostic accuracy, improve efficiency, and expedite patient care.

For example, let’s say you handle 60 X-ray cases a month, and you send out only 10 for teleradiologist review to avoid the expense. A Vetology subscription, which provides unlimited access to AI screening and full reports in as little as five minutes, could support your clinical expertise, helping to confirm your suspicions and streamline decision-making. If you still have doubts about a case, you can escalate it for review by a board-certified veterinary radiologist.

This approach creates a three-tiered approach to patient care, integrating:
• AI insights
• With your professional judgement,
• and expert validation from a radiologist when needed.

Collaborating with the Vetology team can help ensure that your patients receive a timely diagnosis and treatment plan, allowing them to receive the care they deserve quickly.

How you can support accurate AI screening and faster board certified radiologist reports

One of the most important factors that lead to an accurate AI screening is good radiographic technique. Clear, well-positioned, well-developed radiographs are necessary for accurate human and AI interpretation, and the AI does not have the ability to adjust its interpretation based on altered positioning or an unclear image.

For example, if a patient is slightly twisted, anatomical structures may appear distorted on the image. This can lead the AI to misread the size or shape of an organ, or even misidentify a condition. Human radiologists can identify when a patient isn’t perfectly positioned and adjust their interpretation, but AI doesn’t yet have that context—it reads exactly what’s in front of it.

You can take the following measures to increase the likelihood of accurate AI screening:

• Ensure proper positioning of each patient
• Choose the correct radiographic settings to ensure a clear image
• Take at least two views (ventrodorsal and lateral views) of the area to be assessed every time.
• Collimate down to the region of interest to reduce scatter.

Vetology offers personalized, on-demand support tailored to answer your needs and questions. Our team of radiologists and veterinary technicians is always available to provide free, one-on-one guidance with positioning skills and technical assistance (in some cases), whether you’re a seasoned practitioner, a new team member, or a recent graduate.

This article discusses how Vetology’s Radiology AI Tool was created to improve diagnostic accuracy, streamline workflows, and support veterinarians with reliable first-line screening. The system leverages advanced AI methods like CNNs, QA testing, and LLMs for report generation. Designed as a supportive tool, it enhances trust and care quality without replacing expert radiologists. Learn how AI supports veterinary radiology by:

• It is built on a large dataset labeled by board-certified radiologists.
• Using CNNs, confusion matrices, QA testing, and LLMs.
• Improving consistency with preprocessing, cropping, and anomaly detection.
• Undergoing continuous updates with clinical data and structured reviews.
• Enhancing workflow efficiency while leaving final judgment to experts.

When you take a radiograph to better understand a patient’s condition, an accurate reading of the image is key to ensuring the animal receives appropriate treatment. That’s why U.S. board-certified radiologists on the Vetology team work with our data scientists and developers to hone our artificial intelligence (AI) models. By integrating a diverse team of subject matter experts, and combining their skills with 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, validated, and how we iterate on our models.

​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.