The Integration of Artificial Intelligence in Veterinary Anatomy: A Look Ahead in vikasit Bharath
Dr. Triveni T
Assistant Professor, Department of Veterinary Anatomy, Veterinary College Bidar
Abstract
The advent of artificial intelligence has brought profound changes across various industries, from healthcare and finance to education. Within veterinary anatomy education, AI-driven solutions are emerging as a game-changer, offering a multitude of benefits that promise to reshape the way students learn and interact with complex anatomical concepts. AI based Virtual dissection plays important role in alleviating the draw backs of availability of cadavers and ethical consideration. Students and educators must gain important skills in directing and understanding the artificial intelligence technologies. The effective way of integration AI in veterinary anatomy needs collaboration between data scientists, AI specialists, and anatomists.
Key words: Veterinary Anatomy, Artificial Intelligence, Virtual Dissection, Virtual
Staining, Integration.
Introduction:
Artificial intelligence (AI) refers to the ability of computerized systems or computer-controlled robots to execute tasks typically associated with intelligent beings. In essence,AI represents machine intelligence, emulating cognitive functions such as learning andproblem-solving observed in humans and animals. The increasing adoption of AI in education is transforming the learning landscape, yielding substantial benefits across various educational levels and disciplines. Advanced AI-powered tools, including intelligent tutoring systems, tailored learning platforms, and automated assessment solutions, enhance the educational experience by delivering personalized guidance and timely feedback.Mastering anatomy requires strong spatial skills, typically honed through cadaveric dissection. Contemporary teaching methods also leverage advanced 3D imaging techniques including CT scans, MRI, and ultrasound to provide veterinary students with a comprehensive understanding of internal anatomy and organ interactions.The incorporation of AI in anatomy education can yield mutual benefits, fostering students’ professional growth while enabling instructors to innovate teaching strategies, which is especially crucial given the dwindling availability of cadavers in many veterinary institutions. To ensure the long-term viability of AI in anatomy education, key considerations such as cost, training, and reliability must be prioritized. Effective collaboration among educators, technologists, and policymakers is crucial to unlocking AI’s full potential.
Merits of AI in Teaching
Virtual dissection: As part of MSVE 2016 VCI regulation dissection on animals is banned under ethical consideration. Only dissection on cadavers is permitted. But the point to note here is avalaibility cadavers. So virtual dissection overcomes this limitation and enables the students to understand the concept easily. AI also addresses the individual students’ needs and learning styles.
The formaldehyde which is used in preservation of animal bodies is known to cause cancer upon continuous and chronic exposure. So virtual dissection also overcomes this limitation.
Virtual Staining Under Histology:As part of an oncology research project, scientists are pioneering a virtual staining technique to revolutionize histological analysis. This breakthrough method will facilitate the accurate identification of histological boundaries in unstained tissue samples ex vivo, streamlining the process and overcoming the time-consuming limitations of conventional staining techniques.
Merits in clinical/applied anatomy
By recognizing subtle anatomical alterations, AI enables early disease detection and injury diagnosis. AI also mitigates human error in anatomical analysis and generates detailed 3D models, enhancing diagnostic accuracy and streamlining surgical planning.
Merits in forensic veterinary anatomy
As forensic veterinary anatomy is emerging area the application of AI in this stream may play a vital role in identification of species, age and sex of the samples available to anatomists especially with respect to wild animals. There is limited data available on wild life anatomy it can be overcome through the artificial intelligence.
Insights:
Embracing AI in veterinary anatomy education requires a parallel focus on addressing emerging challenges and key considerations. The adoption of AI and digital technologies in education requires a deliberate focus on ethical responsibility, ensuring that teaching practices remain inclusive, equitable, and professionally accountable. Several practical concerns require consideration, particularly those related to logistics, including server expenses, faculty training, tool development, and technical personnel recruitment and management. Even though these facilities are available; students may not possess the skills to use and understand. Proactive evaluation and monitoring of AI systems are critical to identify and address challenges, maintaining peak performance, and delivering accurate and reliable results.It has to be considered that excess usage of automation should not pave the way for loss of critical thinking and deterioration of interpersonal relationships and communicational skills. There are both merits and demerits of integration of AI in teaching but the development should be always on positive way. As we step into the future, let’s drive innovation, adaptability, and growth in veterinary anatomy education, ensuring it remains a standard-bearer for excellence and pioneering achievement.
References:
Abdellatif, H. et al. 2022. Teaching, Learning and Assessing Anatomy with Artificial
Intelligence: The Road to a BetterFuture. Int. J. Environ. Res. Public Health 19, 14209.
Anil Choudhary, 2024. Embracing Tomorrow: The Evolution of Veterinary Anatomy Teaching through Artificial Intelligence,Acta Scientific Veterinary Sciences. vol.6 (5) pp 1-3.
Akinsulie OC, et al. 2024. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Front. Vet. Sci. 11:1347550.
Pandurangam, et al. 2024. AI in anatomy teaching and learning. National Journal of Clinical Anatomy. Vol. 13(3). pp 158-163.
Pushpa, et al. 2022. Will Artificial Intelligence Assume a Role in Anatomy
Education?.National Journal of Clinical Anatomy. Vol.11 (2) pp 65-67.
Romans Maliks and Robert kadikis. Histological recognition and analysis of veterinary tumors surgical margins by using artificial intelligence and multimodal imaging (HAVeT-AI). Ongoing project. Period of time 2023-2025.
Sahar Youssef 2024. Impact of Artificial Intelligence in Anatomy, SAR J Anat Physiol, 5(3), 39-41.
Silvia Burtiet al. 2024. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations.Research in Veterinary Science (175). pp 1-9.
