Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health

Abstract

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is rapidly transforming clinical veterinary practice by enhancing diagnostics, disease surveillance and decision support processes across animal health domains. The safe and effective clinical deployment of these technologies, however, depends critically on the preparedness of the veterinary workforce, positioning veterinary education as a strategic enabler of translational adoption. This narrative review examines the integration of AI within veterinary education as a foundational step toward its responsible application in clinical practice. We synthesize current evidence on AI-driven tools relevant to veterinary curricula, including generative and multimodal large language models, intelligent tutoring systems, virtual and augmented reality platforms and AI-based decision support tools applied to imaging, epidemiology, parasitology, food safety and animal health. Particular attention is given to how the structured educational use of AI mirrors real-world clinical workflows and supports the development of competencies essential for clinical translation, such as data interpretation, uncertainty management, ethical reasoning and professional accountability. The review further addresses ethical, regulatory and cognitive considerations associated with AI adoption, including algorithmic bias, data privacy, equity of access and the risks of overreliance, emphasizing their direct implications for diagnostic reliability and animal welfare. By framing veterinary education as a controlled and reflective environment for AI engagement, this article highlights how pedagogically grounded training can facilitate safer clinical deployment, foster interdisciplinary collaboration and align technological innovation with professional standards in veterinary medicine.

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