Speaker Biography

Ryan Gibson

DVM, M.Ed, DACVIM (Neurology)

Dr. Gibson

Assistant Clinical Professor, Auburn University College of Veterinary Medicine

Ryan Gibson, DVM, DACVIM (Neurology) is a member of the Anatomy, Physiology, and Pharmacology Team within Auburn’s College of Veterinary Medicine and currently teaches within the Microanatomy (Histology) and Neuroscience courses.

Dr. Gibson has a rich interest in neurology/neuroscience and veterinary/higher education pedagogy. After working in private practice, Dr. Gibson returned to academia to focus on his passion for teaching and supporting students both inside and outside of the classroom. He is a strong proponent of mental health initiatives.

Dr. Gibson completed his undergraduate work at the University of Findlay in 2012 with focuses in Biology, Animal Science, and Pre-Veterinary medicine. In 2016 he completed his DVM with Mississippi State University followed by a small-animal rotating internship at The Ohio State University. Following his internship, he completed a private practice specialty internship in neurology/neurosurgery with Gulf Coast Veterinary Specialists in Houston, Texas followed by his residency at Mississippi State University. He became a Board-Certified Diplomate of the American College of Veterinary Internal Medicine – Neurology in 2021. He continues to serve veterinary patients though general practitioner support platforms.

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Lectures

  • Thursday, October 29, 2026 8:00 - 8:50 a.m. Oak II

    Beyond ChatGPT: AI 101 - The different types of AI found in veterinary medicine Artificial Intelligence

    While ChatGPT and other Large Language Model-based systems have become quite popular, in this lecture we will discuss the different types of artificial intelligence and explore their use in veterinary medicine. This will look at large language models and how they work while also introducing machine learning and deep learning modalities and introducing how AI tools are being built with them. Finally, we will share the responsible-use and critical-evaluation approach to AI being taught to students.

    Learning Objectives: 

    1. Describe the difference between LLM, Machine Learning, and Deep Learning
    2. Discuss what AI tools are being built using the different types of AI
    3. Describe responsible use and critical evaluation approaches being taught to students.