Queer Science and Scientists Are Shaping the Future of Immunology.

Artificial intelligence (AI) has moved from a conceptual idea in computer science to a set of practical tools used across medical diagnostics, prognostics and clinical decision support (Khosravi et al. ). In human neurology, AI systems have demonstrated measurable benefits in specific settings, particularly in neuroimaging triage and segmentation, electroencephalography (EEG) analysis and wearable sensor‐based monitoring, although real‐world impact varies by task, data quality and level of clinical integration. These approaches, spanning machine learning (ML) and deep learning (DL), can learn complex patterns from high‐dimensional data and may support faster or more consistent interpretation when validated and deployed appropriately (Esteva et al. ; J. Zhang et al. ; Rizzo ). AI applications in veterinary neurology are an emerging area that is advancing rapidly, in part because companion animals such as dogs and cats develop naturally occurring neurological disorders with clinically relevant parallels to human disease, including gliomas, idiopathic epilepsy, spinal cord injury and Chiari‐like malformation (CM) (Appleby and Basran ; Akinsulie et al. ). This biological and clinical overlap creates opportunities for cross‐species technology transfer, where model architectures, training strategies and representation learning developed in human medicine can be adapted to veterinary contexts. Importantly, translation is potentially bidirectional: Long‐term, real‐world monitoring in canine epilepsy can contribute to the development and robustness testing of seizure detection and forecasting methods; canine gliomas can serve as large‐animal neuro‐oncology imaging models; and quadrupedal gait analysis can broaden biomechanical variability that may improve generalizability of movement analytics (Akbarein et al. ; Albadrani et al. ). This reciprocal perspective aligns with One Health, which emphasizes interconnected human, animal and environmental health and encourages shared infrastructure and governance for data‐driven innovation (Appleby and Basran ; Akinsulie et al. ). However, several barriers limit routine clinical adoption of veterinary neurology AI. Available datasets are often small, heterogeneous and inconsistently standardized across diagnostic equipment, imaging protocols, breeds and institutions (Pomerantz et al. ; Burti et al. ). External validation and prospective evaluation are uncommon, and reporting of clinically relevant metrics such as calibration, uncertainty and error trade‐offs is frequently incomplete (Gulzar and Hussain ; Basran and Appleby ). In addition, veterinary‐specific regulatory pathways for neurology‐focused AI remain limited, and ethical considerations are especially salient for prognostic tools that could influence irreversible decisions, including surgical escalation or euthanasia (Coghlan and Quinn ). Against this background, this narrative review synthesizes AI applications relevant to veterinary neurology within a comparative framework. We summarize advances in neuroimaging and radiomics, electrophysiology and seizure detection or forecasting, gait and pain assessment, morphometric biomarker discovery, prognostic modelling and educational or laboratory tools. Throughout, we emphasize methodological limitations that affect translation, highlight evidence gaps and sources of heterogeneity and outline practical and ethical requirements for moving from proof‐of‐concept performance towards clinically trustworthy deployment. This narrative review was designed to synthesize current evidence on AI applications relevant to veterinary neurology and to compare these developments with analogous advances in human neurology within a One Health framework. Literature searches were performed in PubMed, Scopus, Web of Science and Google Scholar to identify primarily peer‐reviewed articles published between January 2010 and January 2026. Search terms included combinations of ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, ‘veterinary neurology’, ‘canine epilepsy’, ‘seizure detection’, ‘seizure forecasting’, ‘veterinary MRI’, ‘radiomics’, ‘neuroimaging’, ‘gait analysis’, ‘pain recognition’, ‘prognostic modeling’, ‘clinical decision support’, ‘cross‐species translation’ and ‘One Health’. Additional relevant sources were identified by screening the reference lists of key original studies, reviews and position or guidance papers.