Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species...

Artificial intelligence (AI) is increasingly explored in veterinary neurology for pattern recognition, prediction and clinical decision support, with relevance to comparative and translational neuroscience.

This review examines AI applications in veterinary neurology, compares them with human neurology and evaluates cross-species technology transfer within a One Health framework.

A narrative review synthesized evidence across AI domains in veterinary neurology, including neuroimaging and radiomics, electrophysiology and seizure detection or forecasting, gait and pain assessment, morphometric biomarker discovery, prognostic modelling and laboratory diagnostic tools. Human studies were considered to identify translational opportunities, methodological challenges and the value of transfer learning and domain adaptation.

Available evidence suggests that AI holds promise for pattern recognition, prediction and decision support in veterinary neurology. Reported applications include canine brain tumour classification, spinal lesion grading, seizure monitoring and quantitative gait analysis, with encouraging performance. However, most applications remain proof-of-concept. The evidence base is dominated by retrospective single-centre studies with small samples, heterogeneous protocols and limited prospective or external validation. Model calibration, uncertainty reporting and clinically relevant error trade-offs are often insufficiently addressed. Transfer learning and domain adaptation may help overcome limited veterinary datasets, while naturally occurring neurological disease in dogs may also support refinement of human AI systems.

AI in veterinary neurology is a promising but early field. Future progress will require multi-centre collaboration, standardized data practices, explainable and ethically governed models and stronger One Health partnerships to support safe translation.