Smart Learning in Veterinary Science: From Clinical Cases to Digital Tools

Dear Colleagues, 

Veterinary science has long relied on hands-on clinical experience and empirical knowledge. However, the 21st century demands a paradigm shift: we must integrate digital tools into veterinary education and practice. This Special Issue explores how smart learning—driven by artificial intelligence (AI), virtual reality (VR), and data analytics—is transforming veterinary training from case-based learning into immersive, technology-enhanced environments. AI-powered platforms now replicate complex veterinary scenarios, allowing students to practice diagnosing diseases like bovine tuberculosis in a risk-free setting. These simulations bridge theory and practice, reducing learning curves. Tools like AI-assisted radiology and genomic sequencing are revolutionizing disease detection. For instance, deep learning algorithms can analyze X-rays to identify early-stage tumors in dogs, improving treatment outcomes. Big data analytics enables veterinarians to track disease trends, optimize treatment protocols, and predict outbreaks. Traditional veterinary education faces challenges such as limited access to diverse cases and high costs of practical training. Smart learning tools democratize education, making it accessible to remote regions and reducing reliance on physical facilities. Moreover, they prepare students for a future where veterinarians must navigate both clinical skills and digital technologies. 

This Special Issue aims to advance veterinary education and clinical practice by integrating modern digital technologies with traditional case-based learning approaches. Specifically, it seeks to bridge the gap between clinical training and digital innovation‌ by exploring how tools such as artificial intelligence (AI), virtual reality (VR), big data analytics, and online learning platforms can enhance veterinary education, diagnosis, and decision-making. It promotes the adoption of smart learning formats‌, including ‌MOOCs (Massive Open Online Courses)‌ and blended and hybrid learning models. It fosters the development and use of digital clinical tools‌ such as AI-powered diagnostic assistants, imaging analysis systems, virtual patient simulators, and interactive case-based training platforms. It explores data-driven clinical decision support systems that integrate real-time health data, while also encouraging interdisciplinary collaboration‌ among veterinarians, data scientists, educators, and digital health specialists to create innovative, evidence-based educational resources and clinical tools. It supports a One Health perspective‌ by linking veterinary digital learning with broader public health, epidemiology, and environmental health initiatives. 

I look forward to receiving your contributions.

Dr. Lili Hao
Prof. Dr. Guangwen Yin
Guest Editors

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