Background: Radiological image interpretation is a critical yet complex part of veterinary diagnosis, which is somewhat subjective and prone to human error. With increasing patient volume and a limited number of veterinary radiologists, the need for accurate, automated auxiliary tools is growing. Artificial intelligence, particularly deep learning, which can uncover subtle patterns from visual data, has the potential to transform this industry. Methods: The current study was a systematic review, with an extensive search across reputable scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar, from January ۲۰۱۸ to September ۲۰۲۵. The main keywords used were "artificial intelligence", "deep learning", "Machine Learning," "veterinary imaging", and "veterinary radiology". Inclusion criteria were studies that evaluated artificial intelligence algorithms for interpreting radiographic images. After screening titles, abstracts, and full texts, data on diagnostic accuracy (sensitivity and specificity), scope (e.g., fracture detection, osteoarthritis, cardiomegaly), and each study's limitations were extracted and summarized in a narrative synthesis. Results: Of the ۲۱۵ studies identified, ۲۲ met the inclusion criteria. According to the findings, artificial intelligence algorithms, particularly convolutional neural networks (CNNs), achieve high accuracy in diagnosing a wide range of diseases. These diseases include bone fractures, thoracic abnormalities, abdominal anomalies, cardiomegaly, and certain soft tissue lesions on chest and abdominal radiographs. These methods can detect prominent lesions and early disease indications that the radiologist may miss. Conclusion: Veterinary radiologists can benefit from the strength and accuracy of Artificial intelligence. This approach could expedite image interpretation, minimize diagnostic errors, and enhance animal therapy. However, several challenges must be addressed before this technology can be widely adopted in medical facilities. One problem is the need for large, high-quality veterinary photo databases. Other considerations include legal and standardization issues, as well as whether algorithms can be extended across breeds and species. Future research and clinical trials in real-world situations should focus on integrating these technologies into radiology workflows.
