Association between obstructive sleep apnea syndrome and the risk of diabetic retinopathy: a meta-analysis of cohort studies.

Veterinary healthcare remains underserved compared to human healthcare due to fragmented data repositories, limited centralized clinical datasets, reduced access to specialized veterinary services in rural regions, the absence of comprehensive regulatory frameworks for Artificial Intelligence (AI)-enabled veterinary tools, and slower translation of research into clinical practice (, ). Concurrently, the increasing adoption of telemedicine and wearable technologies reflects growing demand for digital veterinary care and proactive health monitoring among pet owners (, ). Recent advances in AI have facilitated veterinary applications in diagnostic imaging, automated triage, predictive epidemiology, disease surveillance, dermatological assessment, chronic disease prediction, and continuous welfare monitoring through wearable technologies (, , , ). These innovations suggest considerable potential to improve diagnostic accuracy and clinical decision-making. However, the integration of AI into routine veterinary practice remains limited as most published studies focus on algorithm development rather than external validation, clinical implementation, explainability, workflow integration, or regulatory requirements (, ). Ongoing challenges, such as breed-specific data bias, limited transparency of “black-box” algorithms, automation bias, and the risk of clinically inappropriate recommendations, continue to hinder the safe adoption of AI (, ). Although several reviews have summarized specific AI applications in veterinary medicine, the literature remains fragmented. Most existing reviews focus on individual application domains or provide narrative overviews without the transparent study selection and reproducible methodology expected of PRISMA-based systematic reviews (, –). To our knowledge, no recent PRISMA-based systematic survey has comprehensively synthesized AI methodologies, validation strategies, implementation readiness, emerging large language model (LLM) applications, and commercially available veterinary AI systems across multiple domains.