By addressing the current knowledge gap through structured and ethical AI training, associate veterinarians and veterinary students will become confident and capable in integrating AI tools, ultimately advancing patient care, optimizing clinical efficiency, and advancing veterinary science.

As an assistant professor at Texas A&M University, I'll offer a perspective grounded in both academic and practical experience throughout the following article, drawing from my research in AI diagnostics and my contributions to national discussions on AI education through invited talks and service on the AVMA Task Force and AAVMC AI Working Group.

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The Growing Role of AI in Veterinary Practice

The presence of AI in veterinary medicine has expanded significantly in recent years, empowering practitioners to perform complex tasks more efficiently and accurately. In my review article, "ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research," I explore the growing role of large language models and other generative AI tools across the veterinary profession.

In the article, I outline how AI can support veterinarians in clinical decision-making, client communication, and recordkeeping, while also enhancing teaching strategies and research productivity. From drafting discharge instructions and summarizing literature to aiding in study design and grant writing, generative AI is transforming how veterinary professionals interact with information. The article also discusses the limitations and ethical considerations of AI adoption, including the importance of transparency, critical oversight, and responsible integration.

The pervasive role of AI underscores the necessity of educational frameworks, ensuring future veterinarians can effectively utilize these powerful tools while recognizing their limitations.

Essential Areas of AI Training for Veterinary Students

First, veterinary students need foundational knowledge of key terminology, including AI, machine learning, generative AI, and large language models (LLMs). They should become familiar with prominent commercial LLMs, such as ChatGPT, Gemini, Grok, and Claude. Additionally, future veterinarians will likely encounter commercial AI scribe tools, like ScribbleVet, CoVet, VetRec, Talkatoo, VetGeni, and Scribenote.

Second, students must be educated about AI-assisted diagnostic tools currently available in veterinary medicine. AI-driven imaging and diagnostic technologies are increasingly integral to veterinary radiology, cytology, and clinical assessments. Students should understand that these tools are designed to augment veterinarians rather than replace them, as veterinarians themselves train these AI systems. For example, AI simplifies complex urine sediment analyses, significantly streamlining daily workflows, and recent advancements in AI-driven imaging and point-of-care diagnostics have considerably improved the efficiency and accuracy of veterinary care.

Last but not least, veterinarians must comprehend that AI tools are not flawless. LLMs are probabilistic models, meaning their outputs are generated based on statistical likelihood rather than absolute rules. As a result, they can sometimes produce plausible yet inaccurate or entirely fabricated information—a phenomenon commonly referred to as "hallucination." Furthermore, veterinary students must be exposed to cases illustrating AI misuse to fully appreciate the potential detrimental impacts on patient welfare when ethical guidelines are disregarded.

Addressing Gaps in Veterinary AI Education

To the best of my knowledge, among 30 AVMA-accredited veterinary colleges, the University of Wisconsin–Madison is currently the only institution offering an elective titled "Artificial Intelligence Applications for Veterinary Practice" to fourth-year veterinary students. Unfortunately, in-school learning opportunities on this topic remain limited, and the AI-related course content offered at individual institutions is not always publicly available.

Recognizing this educational gap, I've developed a new elective course, "Artificial Intelligence and Digital Tools for Next-Generation Veterinarians," approved by the curriculum committee at Texas A&M College of Veterinary Medicine & Biomedical Sciences. Scheduled to launch in Spring 2026, this course specifically targets second-year veterinary students, providing comprehensive AI training tailored to veterinary medicine. The course content is targeted to be housed online and will be shared with educators and students worldwide.

The syllabus includes foundational concepts, like prompt engineering, scientific communication, and ethical considerations related to AI usage. Through interactive lectures, practical demonstrations, and a hands-on final project, students will explore AI-powered clinical diagnostic tools, productivity aids, advanced literature search techniques, and digital note-taking resources. Additionally, students will gain proficiency with digital tools, such as Zotero for citation management, Heptabase for note-taking, and NotebookLM for AI-assisted research organization. This hands-on exposure ensures students are well-prepared to integrate technology seamlessly into their veterinary practice.

Moreover, the course emphasizes ethical AI application, an essential yet often overlooked aspect of technical training. Ethical considerations include addressing algorithmic bias, data privacy concerns, and transparency in AI-assisted diagnostics and understanding the legal responsibilities veterinarians hold in AI-integrated scenarios. Veterinarians must responsibly navigate these ethical complexities, balancing technological efficiency with patient welfare and client trust.

AI Learning Resources for Practicing Veterinarians

Practicing veterinarians know that training never stops, since there's always something new to learn. Training opportunities in AI and LLMs in veterinary medicine are available through videos, online courses, continuing education, and webinars—some of them are even free! For example:

• The Veterinary Information Network (VIN) has a course (free for members) on ChatGPT in veterinary medicine.
• The American College of Veterinary Internal Medicine (ACVIM) has a one-hour course titled "How I Streamline Academic Writing with AI and Digital Tools" that's also free for members.
• YouTube videos can be a great resource, including Streamlining Academic Writing with AI and Digital Tools for Enhancing Productivity, Build Your Own ChatGPT, and Essential AI and Digital Tools for Graduate Students and Veterinary Researchers.
• Continuing education talks at conferences like the ACVIM Annual Forum and the American College of Veterinary Pathologists Annual Meeting can be a fantastic resource to stay up to date.
• Find a webinar on AI for veterinarians. I presented an IDEXX Specialist Webinar titled, "Essential AI and Digital Tools for Veterinary Researchers."

In addition to these accessible efforts, universities remain pivotal in comprehensive veterinarian training, so don't hesitate to check out your alma mater or a nearby university for post-graduate courses if available.

Preparing for an AI-Driven Future

Artificial intelligence (AI) in veterinary medicine is rapidly transforming, revolutionizing diagnostics, patient care, and client communication. Despite AI's potential, many veterinarians remain uncertain about integrating the technology into their practice. Therefore, it's critical to equip veterinary students and associate veterinarians with comprehensive AI training to bridge this knowledge gap effectively.