Artificial Intelligence in Veterinary Medicine: From Laboratory Accuracy to Clinical Readiness — Secondary Analysis of 39 Deep-Learning Studies, a Real-World Benefit–Harm Appraisal, and the Veterinary Translational Readiness Index (VTRI)

Abstract

Background. Artificial intelligence (AI) is moving rapidly from proof-of-concept to routine veterinary use, yet published accuracy figures come mostly from controlled studies, while independent evaluations of commercial tools in general practice are few. No analysis has set laboratory performance, real-world performance and the maturity of the evidence side by side. Methods. We anchored the review on a recent PRISMA-based systematic review of deep learning in veterinary diagnostics (422 PubMed records screened, 39 primary studies, 2013-2024) and performed a secondary quantitative analysis of those 39 studies (modality, species, sample size). We added targeted 2019-2026 evidence on commercial radiology AI, AI devices, large language models (LLMs), clinical documentation tools, adoption surveys, market reports and regulatory statements, and classified each figure by evidence type (peer-reviewed, independent, vendor-reported, projection). We developed the Veterinary Translational Readiness Index (VTRI), a five-domain, 15-point rubric of evidence maturity, paired with a three-level Real-World Performance (RWP) flag. Results. Radiography and cytology each account for 33% of studies; 82.1% addressed dogs or cats and 10.3% livestock or horses. Median sample size was 837.5 (IQR 275-2,355); 52.6% of studies used fewer than 1,000 cases, and datasets were not larger after 2021. None of the 39 studies addressed interpretability technically. Laboratory accuracy of 82.7-99% contrasted with independent evaluations of six commercial platforms on canine abdominal radiographs (sensitivity 28-78%; small-intestinal obstruction 23-69%), a commercial pulmonary-nodule tool (sensitivity 55.4%), an AI stethoscope (atrial-fibrillation PPV 21.4%) and AI triage (about 60% of non-urgent cases over-triaged). Reported adoption rose from 39.2% (2024) to 83.7% (2026) in North American surveys, while specialty colleges stated that no commercial imaging product met transparency, validation and safety standards. VTRI scores ranged from 2 to 7 of 15; interpretability scored 0 in every class. Conclusions. AI is beneficial in narrow, well-labelled tasks, but adoption has outpaced independent validation, interpretability and regulation. Deployment should be clinician-supervised decision support with external validation, prevalence-aware reporting and local validation before importation, particularly in low-resource regions.