Advanced Dissection Lab for Neuroanatomy Training

White Matter Dissection: Lessons from the United Kingdom National Neuroanatomy Undergraduate Competition 2023

Objective  This narrative highlights a student-led initiative that explored white matter dissection techniques within the framework of the National Undergraduate Neuroanatomy Competition 2023. Materials and Methods  The project aimed to enhance neuroanatomical education by developing a novel approach to dissection that deviates from the traditional Klingler's method. Instead, it incorporated contemporary techniques, including diffusion tensor imaging and other radiological tools, to ensure greater anatomical precision and enrich the learning experience. Results  The dissections focused on key white matter structures, such as the uncinate fasciculus and the inferior longitudinal fasciculus, and outlined a step-by-step methodology for creating high-quality specimens. These specimens are designed to serve as educational resources, particularly for students with limited access to formal neuroanatomy courses. Conclusion  This study emphasizes the critical role of hands-on dissection in neuroanatomy education, showcasing its ability to enhance student engagement, deepen anatomical understanding, and inspire interest in clinical neuroscience careers. By introducing innovative educational tools and methodologies, this initiative makes a meaningful contribution to addressing the issue of "neurophobia" in medical education.

Exploring the potential and limitations of artificial intelligence in animal anatomy

BACKGROUND

Artificial intelligence (AI) is revolutionizing veterinary medicine, particularly in the domain of veterinary anatomy. At present, there is no existing review article in the literature that examines the prospects and challenges associated with the use of AI in animal anatomy education.

STUDY DESIGN

Narrative review.

OBJECTIVE

This review article explores the prospects and drawbacks of AI applications in veterinary anatomy. Anatomy and AI-powered diagnostic systems enhance clinical examination, diagnosis, and treatment by analyzing vast datasets, improving accuracy, and detecting subtle anomalies.

METHODS

We reviewed and analyzed recent literature on AI applications in veterinary anatomy education, emphasizing their potential, limitations, and future directions..

CONCLUSION

In veterinary anatomy education, AI integrates advanced tools like three-dimensional (3D) models, virtual reality (VR), and augmented reality (AR), offering dynamic and interactive learning experiences to students as well as the faculty of veterinary institutions across the globe. Despite these advantages, AI faces challenges such as the need for extensive, high-quality data, potential biases, and issues with algorithmic transparency. Additionally, virtual dissection and educational tools may impact hands-on learning and ethical and legal concerns regarding data privacy must be addressed. Balancing AI integration with traditional skills and addressing these challenges will maximize AI's benefits in veterinary anatomy and ensure comprehensive veterinary care.