Artificial Intelligence in Veterinary Radiology: A Narrative Review of Technical, Diagnostic, and Clinical Integration Challenges

Abstract

The integration of artificial intelligence (AI) into veterinary radiology holds significant promise for enhancing diagnostic accuracy, reducing interpretation time, and addressing workforce shortages in specialized imaging. This article reviews current literature to identify and characterize the major challenges hindering seamless clinical translation. A comprehensive literature search was conducted to identify relevant studies addressing the challenges of artificial intelligence in veterinary radiology. The electronic databases PubMed, Scopus, Web of Science, and Google Scholar were systematically searched from January 2020 to June 2026, using combinations of the keywords. Technically, the lack of large-scale, standardized, annotated veterinary image datasets, compared with those in human medicine, severely limits the development and validation of robust deep learning models. Heterogeneity in patient positioning, breed-specific anatomical variations, and differences in imaging equipment across veterinary practices further compound this issue. Diagnostically, AI models trained predominantly on human radiographs often fail to generalize to veterinary patients due to fundamental differences in physiology, common pathologies (e.g., gastric dilatation-volvulus, feline bronchial disease), and species-specific artifacts. Moreover, the challenge of detecting subtle or early-stage lesions in multiple species without species-optimized algorithms raises concerns about false negatives and clinical safety. Clinically, integration into real-world workflow poses substantial hurdles: most commercial AI tools are not embedded within existing practice management software or PACS (Picture Archiving and Communication Systems), requiring manual toggling between platforms. Cost-effectiveness remains unproven for many small to medium-sized clinics, and there is a notable lack of prospective validation studies demonstrating improved patient outcomes or client satisfaction. Additionally, medicolegal and ethical uncertainties persist regarding accountability when AI-generated recommendations conflict with a clinician’s judgment, particularly in emergency settings. Resistance to adoption among general practitioners, stemming from inadequate AI literacy and fear of over-reliance, further slows progress. Finally, this review highlights that, unlike human medicine, veterinary radiology lacks standardized benchmarks, regulatory frameworks, and continuing education programs tailored to AI applications.