A systematic audit of transparency and validation disclosure in commercial veterinary artificial intelligence.

Abstract

OBJECTIVE: To systematically identify the commercial market for clinical artificial intelligence (AI) products in veterinary medicine and audit their public documentation for transparency using a standardized, evidence-based instrument. METHODS: A cross-sectional systematic audit of commercial AI tools was completed via a multi-channel search. Inclusion criteria required commercially available products with explicit AI claims and clinical functionality; administrative and direct-to-consumer tools were excluded. Publicly available documentation was archived and evaluated using a 25-point framework adapted from FDA and GMLP guidelines to assess data provenance, validation, safety, and usability. RESULTS: Seventy-one AI products, available in the North American market were included, comprising Generative and Ambient (= 47), Diagnostic Imaging (= 19), and Specialized tools (= 5). The mean unweighted transparency score across the cohort was 6.4%. Notably, 63.3% (= 45) of vendors failed to disclose a single metric. Diagnostic Imaging tools achieved a higher mean risk-weighted transparency score (13.1%) compared to Generative and Ambient tools (1.8%). While 36.8% of imaging vendors provided peer-reviewed or internal validation evidence, only 2.1% of generative vendors did so. Only one vendor (1.4%) disclosed training data signalment (species, breed, age, sex) or subgroup performance. CONCLUSIONS: The commercial veterinary AI market operates with systemic opacity. This audit reveals a significant "Transparency Gap"-a divergence where the sophisticated clinical capabilities marketed to veterinarians far exceed the publicly available evidence required to validate them. A significant gap exists between maturing imaging applications and unvalidated generative tools. The universal failure to report training demographics renders independent assessment of algorithmic bias impossible. CLINICAL RELEVANCE: Veterinarians currently bear the legal and ethical burden of validating AI tools without access to necessary performance data. The implementation of standardized transparency frameworks is urgently required to support evidence-based product selection and prevent patient harm from unvalidated technologies.