Clinical Validation across 83 Radiographic Condition Classifiers
AI Condition Classifier PerformanceVeterinary AI Classifier Performance Metrics
83+ Condition Classifiers Built on 300,000+ Expert-Reviewed Veterinary Imaging Cases
Vetology’s AI classifiers have been validated across 83 conditions using over 300,000 cases from real-world veterinary practices. Below you’ll find transparent performance data including sensitivity, specificity, Radiologist Agreement Rates and case counts for every classifier. This deep, real-world dataset helps our AI support veterinarians with reliable screening results at the point of care.
Unlike other veterinary AI imaging platforms, we publish complete performance metrics to help you make informed decisions about diagnostic accuracy and clinical implementation.
While these results offer meaningful insight into expected performance, real-world factors such as image quality, positioning, and patient variability can influence accuracy in clinical settings.
What Makes Our Data Different
Understanding Our AI Virtual Radiologist Report Screening Metrics
Why Radiologist Benchmarking Matters
Instead of comparing AI to a theoretical “perfect” standard, Vetology benchmarks performance against the Radiologist Agreement Rates: how often multiple boarded radiologists reach the same interpretation. This provides realistic context for conditions where even experts may disagree.
When sensitivity or specificity approaches or exceeds this rate, it indicates performance comparable to specialist-level interpretation for that finding.
These measures help you understand when AI can reinforce diagnostic confidence and when a case may benefit from further review.
Making Sense of the Metrics
Sensitivity
How reliably the AI detects a condition when it is present.
(True Positive Rate)
The proportion of actual positive cases (condition present) that the AI correctly identifies. For example, 89% sensitivity means the AI correctly detects the condition in 89 out of 100 cases where it is actually present. Higher sensitivity reduces false negatives—cases where a condition exists but goes undetected.
Specificity
How accurately the AI rules out a condition when it is absent.
(True Negative Rate)
The proportion of actual negative cases (condition absent) that the AI correctly identifies. For example, 92% specificity means the AI correctly rules out the condition in 92 out of 100 cases where it is actually absent. Higher specificity reduces false positives—cases where the AI flags a condition that is not present.
Radiologist Agreement Rate
"RAR" in the tables below
The percentage of cases where multiple board-certified veterinary radiologists independently arrive at the same diagnosis. This serves as a benchmark for inherent diagnostic difficulty.
Conditions with lower Radiologist Agreement Rates are more subjective or challenging, even for specialists. When AI performance approaches or exceeds the Radiologist Agreement Rate, it demonstrates specialist-comparable accuracy.
Total Test Cases
Vetology's Published Metrics
Our industry-leading transparency allows you to evaluate our technology objectively.
- Real-World Validation: Tested on 300,000+ actual clinical cases from veterinary practices.
- Radiologist Benchmarking: Performance compared directly to board-certified veterinary radiologists
- Comprehensive Coverage: Canine and feline imaging across thorax, abdomen, spine, and musculoskeletal studies
Legend
Frequently Asked Questions
How accurate are Vetology's AI classifiers?
What conditions can Vetology's AI detect?
We detect 83 conditions including heart failure (left and right), cardiomegaly, pleural fluid, hepatomegaly, splenomegaly, IVDD, hip dysplasia, and more across canine and feline patients. Our classifiers cover thorax (20 canine, 15 feline), abdomen (25 canine, 14 feline), and spine/musculoskeletal (9 conditions) imaging.
How does Vetology compare to other veterinary imaging AI platforms?
What is Radiologist Agreement Rate (RAR)?
Radiologist Agreement Rate measures how often our AI classifiers agree with board-certified veterinary radiologists on the same cases. This benchmark helps practices understand how AI performance compares to specialist interpretation and provides context for clinical decision-making.
How was this data validated?
All performance metrics are based on a rigorous testing process using over 300,000 real-world veterinary cases.
Each classifier was evaluated independently, with sensitivity, specificity, and case counts calculated from actual clinical imaging studies.
Radiologist Agreement Rates compare AI predictions against board-certified veterinary radiologist interpretations.
Ready to Experience AI-Assisted Radiology?
See how Vetology’s classifiers can improve diagnostic confidence and workflow efficiency in your practice.