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

## TL;DR

This paper reviews how AI is being integrated into veterinary education to prepare future veterinarians for using these technologies safely and ethically in clinical practice.

## Contribution

The paper provides a comprehensive review of AI tools in veterinary education and emphasizes the role of education in enabling responsible AI use in clinical settings.

## Key findings

- AI tools like generative models and decision support systems are being used in veterinary education for imaging, epidemiology, and animal health.
- Veterinary education can help students develop competencies in data interpretation, ethical reasoning, and managing uncertainty related to AI.
- Challenges such as algorithmic bias, data privacy, and overreliance on AI must be addressed to ensure safe clinical deployment.

## Abstract

Artificial intelligence (AI) is increasingly used in veterinary medicine to support diagnosis, disease surveillance and clinical decision-making. To ensure that these technologies are applied safely and responsibly in practice, veterinarians must be adequately trained during their education. This review explores how AI is currently being incorporated into veterinary curricula and how the educational use of AI can prepare students for future clinical applications. We describe the main types of AI tools used in veterinary education, including generative and multimodal models, virtual and augmented reality, and decision support systems across areas such as imaging, epidemiology, parasitology, food safety and animal health. The review also highlights important challenges related to ethics, data privacy, bias and overreliance on AI. Overall, we show that veterinary education plays a key role in bridging technological innovation and clinical practice, helping future veterinarians to develop the skills needed to use AI in a safe, ethical and clinically meaningful way.

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.