Description
Abstract
The incorporation of artificial intelligence (AI) in the teaching of veterinary anatomy is modifying conventional approaches to teaching and providing new approaches to addressing conventional problems. This review aims at exploring artificial intelligence to enhance the accuracy as well as enhancing the learning activities in classroom setting with reference to the constraints such as shortage of human cadavers, and variations in manual training techniques.
Virtual and augmented reality and 3D modeling software as well as adaptive learning systems have emerged and enhanced the knowledge delivery to students as accurate, adaptive and more real in many a cases. It can be seen that the implementation of AI has advantages: accessibility, standardization, and motivation of students but there are also issues: the cost, the ethical issue, and requirements for IT skills. This article includes case studies, evaluates the effects of AI devices in veterinary anatomy coursework, and gives an outlook for future prospects and directions for research that will contribute to the improved application of AI tools in educating veterinary anatomical structures and problems appropriately and efficiently.
Introduction
Veterinary anatomy can therefore be described as one of the fundamental cornerstones of veterinary education, which offers indispensable foundational theories for clinical as well as surgical practises. Historically, veterinary anatomy education has involved cadaveric and dread structure along with physical models since they give exquisite spatial sense movement that is necessary for perception of morphology. However, these methods encounter several constraints such as ethical issues on how samples of dead animals are acquired, restriction in the availability of samples, and high cost to sustain necropsy unit (Salazar & Miglino, 2022). Also, the opinion arises as to the inconsistency in the educational results due to the variability of the specific anatomical samples (Sattar et al., 2022).
However, these problems have become more pronounced due to the COVID-19 pandemic and demand crossover and scalable education solutions. Distance education in anatomy has been criticized for its inability to offer practical experience, and thus, the use of new technologies such as artificial intelligence (AI) (Busch et al., 2024). Expert systems have become innovative technology in the teaching of veterinary anatomy where concept individualization and sharing can be achieved via the creation of physical models based on computer technologies and virtual reality and/or augmented reality-based learning. For example, applications like 3D simulators or AI-pushed adaptive learning platforms have improved the plausibility of anatomical education and contributed to creating equal learning environment for all student learner (Aghapour & Bockstahler, 2022). AI also helps with engagement problems by introducing game-based learning and engaging visual displays, which help better and more fun teach anatomy (Choudhary et al., 2023).
Role of artificial intelligence in teaching veterinary anatomy
Technological innovation
AI has started using new and improved methods including virtual reality (VR), augmented reality (AR.) and 3D modeling in the teaching of veterinary anatomy. These technologies offer an experiential feature and enable a learner have a view of the interior structures of the body. AR augments digital information over the conventional tangible world and natural conditions whereas VR sets pictorial frameworks in innovative artificial conditions which makes condition understanding easy for students in terms of spatial relation (Kannan et al., 2024). AI involves in the AR/VR digital models to control and explore them in real-time improves students’ attention and comprehension of the models and their subjects (Atanasova, 2021).
Accuracy and precision
Requirements for AI include: AI tools distinguish anatomical structures with high accuracy, and standardize the presentation of instructional materials; 3D reconstructions and AI-produced models ensure students manipulate parts, organs, and other sections that are as close to actual cadaver form as possible but with limited variability. AI algorithms also contribute to the generation of additional datasets reflecting anatomical variations, as well as broadening the learning field (Henssen et al., 2024). Furthermore, with the help of augmented reality (XR) applications, the instructors can explain minute structures not easy to visualise (Ehlers et al., 2024).
Personalized Learning
Adaptive learning programs based on Artificial Intelligence has enhanced on the teaching of veterinary anatomy even with the student’s curriculum abilities. These systems track the performance of students and subsequently the difficulty level of the lessons is set in order to facilitate learning with efficiency at an average pace (Pandurangam et al., 2024). Besides, through applying artificial intelligence in learning, gamified applications support self-organized learning by providing anatomical difficulties in form of appealing games, thereby enhancing motivation as well as knowledge advancements (Sun et al., 2024).
Engagement through AI
Interactive Teaching
Multiplayer games for anatomy, and AI-based anatomy applications that include AR and VR have transformed the way that veterinary anatomy is taught. Gamification incorporates features like point system, badges and competition and other features that helps in effective participation of the students. For example, Kumar et al. (2023) investigated the impact of implementing interactive 3D. Modern VR applications, for example, Anatomage, lets students ‘dissect’ practically and allows to zoom into the structures, which is especially important when examining complicated topology areas (Dekker et al., 2024).
A student-centered approach
It has been established that incorporating artificial intelligence in providing feedback to students during the actual course of learning sharpens the learning process due to quick correction and additional notes offered. Machine learning programs and interactions of the students check the areas in need of improvement and change content accordingly. In their study Lewis et al. (2024), stated that the use of games for educational programmes containing real time assessment and feedback facilitate independent and pinpoint learning. Moreover, these systems help with tracking the effectiveness of the teaching and learning processes over time, and provide teachers with information concerning performance and development issues of their learners.
Virtual platforms made coordinated dissection exercises and problem solving activities realizable hence promoting collaboration despite the distributed environment. Ryan et al. (2023) provided a virtual anatomy dissecting table through which students could not only highlight structures in real time, but also write notes on the screen that other learners could see. Wiki-based anatomical atlases enhance group participation and expertise (Previdelli et al., 2022) have also been found to enhance discussion and understanding of general group on different issues. AI-enabled systems enhance opportunities that have been restricted based on geography and time and make learning seamless and engaging for all stakeholders.
Proper assimilation of AI: Advantages and Challenges
Advantages
The addition of AI technology inside veterinary anatomy class has benefits such as, accessibility, scalability and effectiveness for the students. Virtual dissection tools and 3D models can mean anatomical resources are available and they provide an understanding of those resources without regard to a student’s geographical location or school (Salazar & Miglino, 2022). Moreover, these resources may be used with a greater number of students easily without requiring increased access to specimens and dissection tools (Allsop & SFHEA, 2023). According to Lustgarten et al (2020) authors, the application of artificial intelligence intervened as remarkable in designing and delivering a learning path tailored to a learner’s capacities and learning difficulties for enhanced learning. Higher retention rate and course participation also can be associated with gamified AI learning environment that enhances the stimulating and playful experiences (Choudhary et al., 2023).
Challenges
As beneficial as bringing artificial intelligence into play for the teaching of veterinary anatomy may be, it also came with some challenges. Large investments required for the use of AI together with the need to constantly update these assets are some of the biggest financial challenges (Banzato et al., 2024). At the same time, educators would require technical training to be able to implement and use AI systems in classroom instruction which may be an uphill task (Wilson et al., 2022). Some of the ethical challenges are; passing of human touch, here in cadaver-based learning coupled with over-reliance on AI for important things like thinking (Busch et al., 2024). Some of the previous challenges still persist, such as the need to interpret the artificial intelligence systems such that educators as well as students can have confidence in the systems to deliver accurate results (Albadrani & Abdel-Raheem, 2024).
Case studies and applications
These technologies include the Anatomage Table where faculty can teach veterinary anatomy and augmented and virtual reality applications including HoloAnatomy which has become a game changer. They incorporate enhancements in visualization that enable a student to move, joy-ride, essentially, over the exciting world of human, animal or even plant body systems. The following examples illustrate their adoption and impact:
• Case Western Reserve University: From the ground-up created in collaboration with Microsoft Corporation the HoloAnatomy® application suite is changing the way anatomy is taught through the use of augmented reality (AR). It can also be explained that veterinary and medical students can use 3D holographic projections to help strengthen the concepts of the relative location of structures. Research by Case western Reserve University predictable that this platform increase retention and engagement, and decrease cadaver dependent (2023).
• Texas A&M University: Texas A&M combined Microsoft’s HoloLens with HoloAnatomy Suite and incorporated AR for enhanced training of anatomy. The program avail the students get an experience of merging vascular and skeletal systems, interactively as if participating in an actual dissection in the real time, real people autopsy (Texas A&M IT News, 2024).
• Anatomage VR: Among the new technologies currently implemented in veterinary education, Anatomage VR includes 3D anatomy models and various interaction tools. Several veterinary institutions have adopted the tool in blended learning teaching especially in comparative anatomy. Some of the comments on its use include that it has a smooth user interface and its effect in enhancing correct learning (Auganix, 2024).
• University of Oxford: AR/VR for veterinary and human anatomy has also gained international attention. The implementation of the site HoloAnatomy in Oxford facilitated the distinction of anatomy in different species and make it more interprofessional in the programs for the veterinarian. According to XR Today (2023), the simulation capabilities of the tool have enhanced the pretend autopsy performances of the trainees significantly.
New Trends in Artificial Intelligence on the Anatomic Modeling
Recent development in usability of artificial intelligence particularly generative artificial intelligence provides effective opportunities for teaching veterinary anatomy. Some deep learning frameworks like generative AI algorithms are efficient in designing highly detailed anatomical models thus enhancing the realism of the discernment of the human body structures. For instance, deep learning has been applied for modeling traumatic brain injury and enhancing anatomical knowledge base combines with interdisciplinary ones (Shi et al., 2024). Likewise, other big animals like pigs and dogs are also getting combined with the help of predictive AI modeling to work out data for the pertinent educational application (Khushtar et al., 2024).
Conclusion
The concept that has revolutionized knowledge delivery and receipt in veterinary anatomy is AI or artificial intelligence. Through the incorporation of artificial intelligence, the process of augmenting reality (AR), virtual reality (VR) and mechanisms with deep learning educationists have been in a position to make teaching more accurate engaging and interactive. These changes have enhanced the imaging of anatomical structures, and afforded student practical and enhanced learning environments that mimic real-life situations (Saeed et al., 2023).
The revolutionary progressing in the area of radiography like interpretation with application of machine intelligence as well as the anatomical labelling notably extended the learning outcomes effectiveness. Such tools enhance and in fact respond to some of the issues experienced with conventional instruction, including the changeability of dissection materials and the availability of the specimens from live organisms (Norena, 2023). Likewise, in terms of pedagogy, it demonstrates capabilities in the process of adopting learning technology to analyse personal differentiation, delivery of content based on students’ learning profiles, and learning speed (Wilson & Lazarus, 2023).
Dr Santosh Kumar Sahu
Assistant professor, Veterinary anatomy, Institute of Veterinary Science and Animal Husbandry, SIKSHA 'O' ANUSANDHAN (Deemed to be University), Bhubaneswar, Odisha
References
Aghapour, M., & Bockstahler, B. (2022). State of the art and future prospects of virtual and augmented reality in veterinary medicine: A systematic review. Animals, 12(24), Article 3517. DOI: 10.3390/ani12243517.
Allsop, S., & SFHEA, B. (2023). Educational alternatives for veterinary anatomy teaching. Use of Animals in Veterinary Education Handbook, 15(2), 203-219.
Auganix. (2024). Anatomage VR brings interactive anatomy learning to the metaverse. Augmented Reality News.
Atanasova, T., & Petrov, P. (2021). Digital twins with application of AR and VR in livestock instructions. Problems of Engineering Cybernetics and Robotics, 72(4), 55-64.
Banzato, T., Coghlan, S., & Wodziniski, M. (2024). Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations. Research in Veterinary Science, 144(1), 43-55. DOI: 10.1016/j.rvsc.2024.04.001.
Busch, F., Hoffmann, L., Truhn, D., & Ortiz-Prado, E. (2024). Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties. BMC Medical Education, 24(1), Article 60. DOI: 10.1186/s12909-024-06035-4.
Case Western Reserve University. (2023). HoloAnatomy® Software Suite. Case Western Reserve University.
Choudhary, O. P., Saini, J., Challana, A., & Choudhary, O. (2023). ChatGPT for veterinary anatomy education: An overview of the prospects and drawbacks. International Journal of Morphology, 41(4), 456-463. DOI: 10.4067/S0717-95022023000400456.
Dekker, E., Whitburn, D., & Preston, S. (2024). Adoption of immersive virtual reality as an intrinsically motivating learning tool in parasitology. Virtual Reality, 28(1), 32-47. DOI: 10.1007/s10055-024-01016-w.
Ehlers, J. P., Schirone, R., & Corte, G. M. (2024). Effects of 3D scans on veterinary students' learning outcomes compared to traditional 2D images in anatomy classes. Animals, 14(15), 2171. DOI: 10.3390/ani14152171.
Faustino-Rocha, A. I., & Lança, M. J. (2024). Teaching, learning, and examination in veterinary anatomy: What do students tell us? University of Évora Repository.
Henssen, D., Karstens, J., & De Jong, G. (2024). Extended reality in anatomy education: Organization, evolution, and assessment of an innovative teaching program. Annals of Anatomy, 243, 151016. DOI: 10.1016/j.aanat.2024.151016.
Joslyn, S., & Alexander, K. (2022). Evaluating artificial intelligence algorithms for use in veterinary radiology. Veterinary Radiology & Ultrasound, 63(5), 849-858. DOI: 10.1111/vru.13159.
Kannan, T. A., Abinaya, P., Dharani, K., & Gnanadevi, R. (2024). Beyond the dissection table: AI-powered virtual anatomy for veterinary students.
Khushtar, M., Khatoon, A., Sachan, V., & Kori, P. (2024). Current practices and emerging technologies in animal models for gastric ulcer research. Journal of Digestive Diseases and Health Sciences.
Koch, J., & Martin, R. J. (2024). Generative AI and its applications in anatomy modeling: Challenges and opportunities. Computational Anatomy Journal, 35(4), 567-581.
Kumar, R., Jain, V., & Touzene, A. (2023). Immersive virtual and augmented reality in healthcare: An IoT and blockchain perspective. Springer. DOI: 10.1007/978-3-031-01065-5.
Lewis, K. O., Popov, V., & Fatima, S. S. (2024). From static web to metaverse: Reinventing medical education in the post-pandemic era. Annals of Medicine, 56(2), 89-102. DOI: 10.1080/07853890.2024.2305694.
Lustgarten, J. L., Zehnder, A., & Shipman, W. (2020). Veterinary informatics: Forging the future between veterinary medicine, human medicine, and One Health initiatives. JAMIA Open,3(2), 306-318. DOI: 10.1093/jamiaopen/ooaa005.
Norena, N. (2023). Evaluation of the use of a deep active learning model in anatomicsegmentation tasks in canine thoracic radiographs. Atrium, University of Guelph.
Pandurangam, G., Gurajala, S., & Singh, K. (2024). Artificial intelligence in anatomy teaching and learning: A literature review. Journal of Clinical Anatomy, 37(3), 203-215.
Previdelli, R. L., Boardman, E., & Channon, S. B. (2022). The canine abdomen wiki dissection as a novel group activity for learning veterinary anatomy. Journal of Anatomy, 241(4), 585-592. DOI: 10.1111/joa.13678.
Ryan, S. M., Bell, S. A., & Simons, M. C. (2023). Technology in the classroom: Advancing anatomy education. Educational Principles of Veterinary Sciences, 18(3), 211-224. DOI: 10.1002/9781119852865.
Salazar, J. M. V., & Miglino, M. A. (2022). Distance education in veterinary medicine: History, current situation, and future perspectives (a systematic review). EaD em Foco, 12(3), 1-15. DOI: 10.24042/eadf.v12i3.4502.
Saeed, M. R., Abdullah, M., Zoraiz, M., Ahmad, W., & Hafeez, A. (2023). Impact of artificial intelligence and communication tools in veterinary and medical sciences: AI in health sciences. AI and Its Applications in Health, IGI Global.
Sattar, A. A., Hoque, M. A., Irin, N., & Charles, D. (2022). Identifying benefits, challenges, and options for improvement of veterinary work-based learning in Bangladesh. Journal of Veterinary Medical Education, 49(2), 170-179. DOI: 10.3138/jvme-2022-0049.
Shi, J., Zhou, Z., Du, X., Cavagnaro, M. J., & Cai, J. (2024). New insights and perspectives on traumatic brain injury: Integration, translation, and multidisciplinary approaches. Frontiers in Neurology.
Wilson, A. B., & Lazarus, M. D. (2023). Potential for AI-assisted medical education using large data models. Academic Medicine, 98(8), 1132-1138.
Wilson, D. U., & Bailey, M. Q. (2022). The role of artificial intelligence in clinical imaging and workflows. Veterinary Radiology & Ultrasound, 63(4), 789-798. DOI: 10.1111/vru.13157.
XR Today. (2023). HoloAnatomy: Microsoft’s MR learning solution, used by Oxford University. XR Today.
The incorporation of artificial intelligence (AI) in the teaching of veterinary anatomy is modifying conventional approaches to teaching and providing new approaches to addressing conventional problems. This review aims at exploring artificial intelligence to enhance the accuracy as well as enhancing the learning activities in classroom setting with reference to the constraints such as shortage of human cadavers, and variations in manual training techniques.
Virtual and augmented reality and 3D modeling software as well as adaptive learning systems have emerged and enhanced the knowledge delivery to students as accurate, adaptive and more real in many a cases. It can be seen that the implementation of AI has advantages: accessibility, standardization, and motivation of students but there are also issues: the cost, the ethical issue, and requirements for IT skills. This article includes case studies, evaluates the effects of AI devices in veterinary anatomy coursework, and gives an outlook for future prospects and directions for research that will contribute to the improved application of AI tools in educating veterinary anatomical structures and problems appropriately and efficiently.
Introduction
Veterinary anatomy can therefore be described as one of the fundamental cornerstones of veterinary education, which offers indispensable foundational theories for clinical as well as surgical practises. Historically, veterinary anatomy education has involved cadaveric and dread structure along with physical models since they give exquisite spatial sense movement that is necessary for perception of morphology. However, these methods encounter several constraints such as ethical issues on how samples of dead animals are acquired, restriction in the availability of samples, and high cost to sustain necropsy unit (Salazar & Miglino, 2022). Also, the opinion arises as to the inconsistency in the educational results due to the variability of the specific anatomical samples (Sattar et al., 2022).
However, these problems have become more pronounced due to the COVID-19 pandemic and demand crossover and scalable education solutions. Distance education in anatomy has been criticized for its inability to offer practical experience, and thus, the use of new technologies such as artificial intelligence (AI) (Busch et al., 2024). Expert systems have become innovative technology in the teaching of veterinary anatomy where concept individualization and sharing can be achieved via the creation of physical models based on computer technologies and virtual reality and/or augmented reality-based learning. For example, applications like 3D simulators or AI-pushed adaptive learning platforms have improved the plausibility of anatomical education and contributed to creating equal learning environment for all student learner (Aghapour & Bockstahler, 2022). AI also helps with engagement problems by introducing game-based learning and engaging visual displays, which help better and more fun teach anatomy (Choudhary et al., 2023).
Role of artificial intelligence in teaching veterinary anatomy
Technological innovation
AI has started using new and improved methods including virtual reality (VR), augmented reality (AR.) and 3D modeling in the teaching of veterinary anatomy. These technologies offer an experiential feature and enable a learner have a view of the interior structures of the body. AR augments digital information over the conventional tangible world and natural conditions whereas VR sets pictorial frameworks in innovative artificial conditions which makes condition understanding easy for students in terms of spatial relation (Kannan et al., 2024). AI involves in the AR/VR digital models to control and explore them in real-time improves students’ attention and comprehension of the models and their subjects (Atanasova, 2021).
Accuracy and precision
Requirements for AI include: AI tools distinguish anatomical structures with high accuracy, and standardize the presentation of instructional materials; 3D reconstructions and AI-produced models ensure students manipulate parts, organs, and other sections that are as close to actual cadaver form as possible but with limited variability. AI algorithms also contribute to the generation of additional datasets reflecting anatomical variations, as well as broadening the learning field (Henssen et al., 2024). Furthermore, with the help of augmented reality (XR) applications, the instructors can explain minute structures not easy to visualise (Ehlers et al., 2024).
Personalized Learning
Adaptive learning programs based on Artificial Intelligence has enhanced on the teaching of veterinary anatomy even with the student’s curriculum abilities. These systems track the performance of students and subsequently the difficulty level of the lessons is set in order to facilitate learning with efficiency at an average pace (Pandurangam et al., 2024). Besides, through applying artificial intelligence in learning, gamified applications support self-organized learning by providing anatomical difficulties in form of appealing games, thereby enhancing motivation as well as knowledge advancements (Sun et al., 2024).
Engagement through AI
Interactive Teaching
Multiplayer games for anatomy, and AI-based anatomy applications that include AR and VR have transformed the way that veterinary anatomy is taught. Gamification incorporates features like point system, badges and competition and other features that helps in effective participation of the students. For example, Kumar et al. (2023) investigated the impact of implementing interactive 3D. Modern VR applications, for example, Anatomage, lets students ‘dissect’ practically and allows to zoom into the structures, which is especially important when examining complicated topology areas (Dekker et al., 2024).
A student-centered approach
It has been established that incorporating artificial intelligence in providing feedback to students during the actual course of learning sharpens the learning process due to quick correction and additional notes offered. Machine learning programs and interactions of the students check the areas in need of improvement and change content accordingly. In their study Lewis et al. (2024), stated that the use of games for educational programmes containing real time assessment and feedback facilitate independent and pinpoint learning. Moreover, these systems help with tracking the effectiveness of the teaching and learning processes over time, and provide teachers with information concerning performance and development issues of their learners.
Virtual platforms made coordinated dissection exercises and problem solving activities realizable hence promoting collaboration despite the distributed environment. Ryan et al. (2023) provided a virtual anatomy dissecting table through which students could not only highlight structures in real time, but also write notes on the screen that other learners could see. Wiki-based anatomical atlases enhance group participation and expertise (Previdelli et al., 2022) have also been found to enhance discussion and understanding of general group on different issues. AI-enabled systems enhance opportunities that have been restricted based on geography and time and make learning seamless and engaging for all stakeholders.
Proper assimilation of AI: Advantages and Challenges
Advantages
The addition of AI technology inside veterinary anatomy class has benefits such as, accessibility, scalability and effectiveness for the students. Virtual dissection tools and 3D models can mean anatomical resources are available and they provide an understanding of those resources without regard to a student’s geographical location or school (Salazar & Miglino, 2022). Moreover, these resources may be used with a greater number of students easily without requiring increased access to specimens and dissection tools (Allsop & SFHEA, 2023). According to Lustgarten et al (2020) authors, the application of artificial intelligence intervened as remarkable in designing and delivering a learning path tailored to a learner’s capacities and learning difficulties for enhanced learning. Higher retention rate and course participation also can be associated with gamified AI learning environment that enhances the stimulating and playful experiences (Choudhary et al., 2023).
Challenges
As beneficial as bringing artificial intelligence into play for the teaching of veterinary anatomy may be, it also came with some challenges. Large investments required for the use of AI together with the need to constantly update these assets are some of the biggest financial challenges (Banzato et al., 2024). At the same time, educators would require technical training to be able to implement and use AI systems in classroom instruction which may be an uphill task (Wilson et al., 2022). Some of the ethical challenges are; passing of human touch, here in cadaver-based learning coupled with over-reliance on AI for important things like thinking (Busch et al., 2024). Some of the previous challenges still persist, such as the need to interpret the artificial intelligence systems such that educators as well as students can have confidence in the systems to deliver accurate results (Albadrani & Abdel-Raheem, 2024).
Case studies and applications
These technologies include the Anatomage Table where faculty can teach veterinary anatomy and augmented and virtual reality applications including HoloAnatomy which has become a game changer. They incorporate enhancements in visualization that enable a student to move, joy-ride, essentially, over the exciting world of human, animal or even plant body systems. The following examples illustrate their adoption and impact:
• Case Western Reserve University: From the ground-up created in collaboration with Microsoft Corporation the HoloAnatomy® application suite is changing the way anatomy is taught through the use of augmented reality (AR). It can also be explained that veterinary and medical students can use 3D holographic projections to help strengthen the concepts of the relative location of structures. Research by Case western Reserve University predictable that this platform increase retention and engagement, and decrease cadaver dependent (2023).
• Texas A&M University: Texas A&M combined Microsoft’s HoloLens with HoloAnatomy Suite and incorporated AR for enhanced training of anatomy. The program avail the students get an experience of merging vascular and skeletal systems, interactively as if participating in an actual dissection in the real time, real people autopsy (Texas A&M IT News, 2024).
• Anatomage VR: Among the new technologies currently implemented in veterinary education, Anatomage VR includes 3D anatomy models and various interaction tools. Several veterinary institutions have adopted the tool in blended learning teaching especially in comparative anatomy. Some of the comments on its use include that it has a smooth user interface and its effect in enhancing correct learning (Auganix, 2024).
• University of Oxford: AR/VR for veterinary and human anatomy has also gained international attention. The implementation of the site HoloAnatomy in Oxford facilitated the distinction of anatomy in different species and make it more interprofessional in the programs for the veterinarian. According to XR Today (2023), the simulation capabilities of the tool have enhanced the pretend autopsy performances of the trainees significantly.
New Trends in Artificial Intelligence on the Anatomic Modeling
Recent development in usability of artificial intelligence particularly generative artificial intelligence provides effective opportunities for teaching veterinary anatomy. Some deep learning frameworks like generative AI algorithms are efficient in designing highly detailed anatomical models thus enhancing the realism of the discernment of the human body structures. For instance, deep learning has been applied for modeling traumatic brain injury and enhancing anatomical knowledge base combines with interdisciplinary ones (Shi et al., 2024). Likewise, other big animals like pigs and dogs are also getting combined with the help of predictive AI modeling to work out data for the pertinent educational application (Khushtar et al., 2024).
Conclusion
The concept that has revolutionized knowledge delivery and receipt in veterinary anatomy is AI or artificial intelligence. Through the incorporation of artificial intelligence, the process of augmenting reality (AR), virtual reality (VR) and mechanisms with deep learning educationists have been in a position to make teaching more accurate engaging and interactive. These changes have enhanced the imaging of anatomical structures, and afforded student practical and enhanced learning environments that mimic real-life situations (Saeed et al., 2023).
The revolutionary progressing in the area of radiography like interpretation with application of machine intelligence as well as the anatomical labelling notably extended the learning outcomes effectiveness. Such tools enhance and in fact respond to some of the issues experienced with conventional instruction, including the changeability of dissection materials and the availability of the specimens from live organisms (Norena, 2023). Likewise, in terms of pedagogy, it demonstrates capabilities in the process of adopting learning technology to analyse personal differentiation, delivery of content based on students’ learning profiles, and learning speed (Wilson & Lazarus, 2023).
Dr Santosh Kumar Sahu
Assistant professor, Veterinary anatomy, Institute of Veterinary Science and Animal Husbandry, SIKSHA 'O' ANUSANDHAN (Deemed to be University), Bhubaneswar, Odisha
References
Aghapour, M., & Bockstahler, B. (2022). State of the art and future prospects of virtual and augmented reality in veterinary medicine: A systematic review. Animals, 12(24), Article 3517. DOI: 10.3390/ani12243517.
Allsop, S., & SFHEA, B. (2023). Educational alternatives for veterinary anatomy teaching. Use of Animals in Veterinary Education Handbook, 15(2), 203-219.
Auganix. (2024). Anatomage VR brings interactive anatomy learning to the metaverse. Augmented Reality News.
Atanasova, T., & Petrov, P. (2021). Digital twins with application of AR and VR in livestock instructions. Problems of Engineering Cybernetics and Robotics, 72(4), 55-64.
Banzato, T., Coghlan, S., & Wodziniski, M. (2024). Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations. Research in Veterinary Science, 144(1), 43-55. DOI: 10.1016/j.rvsc.2024.04.001.
Busch, F., Hoffmann, L., Truhn, D., & Ortiz-Prado, E. (2024). Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties. BMC Medical Education, 24(1), Article 60. DOI: 10.1186/s12909-024-06035-4.
Case Western Reserve University. (2023). HoloAnatomy® Software Suite. Case Western Reserve University.
Choudhary, O. P., Saini, J., Challana, A., & Choudhary, O. (2023). ChatGPT for veterinary anatomy education: An overview of the prospects and drawbacks. International Journal of Morphology, 41(4), 456-463. DOI: 10.4067/S0717-95022023000400456.
Dekker, E., Whitburn, D., & Preston, S. (2024). Adoption of immersive virtual reality as an intrinsically motivating learning tool in parasitology. Virtual Reality, 28(1), 32-47. DOI: 10.1007/s10055-024-01016-w.
Ehlers, J. P., Schirone, R., & Corte, G. M. (2024). Effects of 3D scans on veterinary students' learning outcomes compared to traditional 2D images in anatomy classes. Animals, 14(15), 2171. DOI: 10.3390/ani14152171.
Faustino-Rocha, A. I., & Lança, M. J. (2024). Teaching, learning, and examination in veterinary anatomy: What do students tell us? University of Évora Repository.
Henssen, D., Karstens, J., & De Jong, G. (2024). Extended reality in anatomy education: Organization, evolution, and assessment of an innovative teaching program. Annals of Anatomy, 243, 151016. DOI: 10.1016/j.aanat.2024.151016.
Joslyn, S., & Alexander, K. (2022). Evaluating artificial intelligence algorithms for use in veterinary radiology. Veterinary Radiology & Ultrasound, 63(5), 849-858. DOI: 10.1111/vru.13159.
Kannan, T. A., Abinaya, P., Dharani, K., & Gnanadevi, R. (2024). Beyond the dissection table: AI-powered virtual anatomy for veterinary students.
Khushtar, M., Khatoon, A., Sachan, V., & Kori, P. (2024). Current practices and emerging technologies in animal models for gastric ulcer research. Journal of Digestive Diseases and Health Sciences.
Koch, J., & Martin, R. J. (2024). Generative AI and its applications in anatomy modeling: Challenges and opportunities. Computational Anatomy Journal, 35(4), 567-581.
Kumar, R., Jain, V., & Touzene, A. (2023). Immersive virtual and augmented reality in healthcare: An IoT and blockchain perspective. Springer. DOI: 10.1007/978-3-031-01065-5.
Lewis, K. O., Popov, V., & Fatima, S. S. (2024). From static web to metaverse: Reinventing medical education in the post-pandemic era. Annals of Medicine, 56(2), 89-102. DOI: 10.1080/07853890.2024.2305694.
Lustgarten, J. L., Zehnder, A., & Shipman, W. (2020). Veterinary informatics: Forging the future between veterinary medicine, human medicine, and One Health initiatives. JAMIA Open,3(2), 306-318. DOI: 10.1093/jamiaopen/ooaa005.
Norena, N. (2023). Evaluation of the use of a deep active learning model in anatomicsegmentation tasks in canine thoracic radiographs. Atrium, University of Guelph.
Pandurangam, G., Gurajala, S., & Singh, K. (2024). Artificial intelligence in anatomy teaching and learning: A literature review. Journal of Clinical Anatomy, 37(3), 203-215.
Previdelli, R. L., Boardman, E., & Channon, S. B. (2022). The canine abdomen wiki dissection as a novel group activity for learning veterinary anatomy. Journal of Anatomy, 241(4), 585-592. DOI: 10.1111/joa.13678.
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