Vetology and VetIT are collaborating to bring the benefits of artificial intelligence (AI) in radiology to veterinary practices across the UK. VetIT, a leading provider of veterinary software solutions, is working with Vetology to introduce innovative AI tools that support clinical decision-making, enhance diagnostic accuracy, and streamline radiographic workflows. This partnership reflects a shared vision for advancing veterinary care through technology making sophisticated diagnostic support more accessible to veterinary teams. At the heart of this collaboration lies a powerful AI radiology tool developed by Vetology designed to support vets in making faster, more accurate diagnoses.
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 the U.S. board-certified radiologist on the Vetology team worked in conjunction with the technology crew to develop the artificial intelligence (AI) models. By integrating expert oversight, rigorous testing, and quality assurance measures, AI can enhance diagnostic efficiency while maintaining the trust and reliability vets need when performing radiology.
To help you better understand the Vetology AI radiology tool, this article explains how it was developed and validated, and how it is continuously improved.
Relying on the experts
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-labelled data (i.e., ground truth) that allows the AI to learn from professional interpretations.
Training the AI
To train our veterinary radiology AI tool, we used a combination of deep learning techniques, including convolutional neural networks (CNNs), confusion matrices, quality assurance (QA) regression testing, and large language models (LMMs).
Convolutional neural networks
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 analysed.
- Once trained, a CNN can determine orientation and recognize certain abnormalities and conditions.
Confusion matrices
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 consists of four key components:
The confusion matrix helps refine AI 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
Quality assurance regression testing
QA regression testing compares AI-generated results to known labelled images to identify errors, inconsistencies, and areas for improvement. This allows our developers to fine-tune the AI for better diagnostic precision.
Large language models
LLMs learn common veterinary diagnostic phrases, sentence structures, and condition descriptions to generate professional, structured reports.
Board-certified veterinary radiologists conduct a final review of the reports generated to ensure the AI accurately interprets the images.
AI screening features
In veterinary radiology, AI screening features enhance the efficiency, accuracy, and consistency of image interpretation. Key features include:
- Image preprocessing and standardization – AI adjusts orientation, brightness, and contrast for clearer analysis.
- Anomaly detection – AI identifies abnormalities, such as fractures, tumours, and changes in lung patterns, and can detect species- and region-specific changes. Severity grading models can also help classify the condition’s severity.
- Automated cropping – EfficientDet SSD technology isolates the area of concern, improving diagnostic accuracy.
Keeping updated
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. Vets 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.
See the power of AI in action
As veterinary imaging continues to advance, incorporating AI into your diagnostic workflow isn’t just about keeping up with technology, it’s about gaining a clinical edge. If you’re interested in how Vetology AI can help your team work more efficiently, deliver faster insights, and boost diagnostic confidence, we invite you to take a closer look. Visit the dedicated Vetology AI page on the VetIT website to learn more and book a demo see firsthand how AI-powered radiology can elevate your practice.
