From Cadavers to Neural Networks: A Narrative Review on Artificial Intelligence Tools in Anatomy Teaching

Srinivasa Rao Sirasanagandla, Sharmila Saran Rajendran, Sreenivasulu Reddy Mogali, Yassine Bouchareb, Noushath Shaffi, Adham Al-Rahbi*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

The application of artificial intelligence (AI) in anatomy teaching is gaining attention due to its potential to support personalized learning and its ability to provide customized, real-time feedback. While the potential impact of complete AI integration in medical education remains unclear, there is a suspicion that it could revolutionize pedagogical and assessment practices. Traditional anatomy teaching strategies that use donated human resources hinder continuous learning due to accessibility and ethical challenges. Existing resources, such as anatomy atlases, may not provide knowledge of spatial relationships. AI-powered applications enable students to access more flexible and accessible learning material beyond physical classrooms. This review critically evaluates current advances and the possible impacts of AI in learning anatomy based on the reported empirical original studies. Additionally, it recognizes the challenges and provides possible solutions for them. Most of the initiatives to integrate AI in anatomy teaching are directed towards the development of customized anatomy chatbots and their integration with virtual reality (VR). Although the crucial role of medical imaging in the anatomy curriculum is recognized, currently, no AI application has been developed to target this field. This review discusses the currently available AI tools for anatomy teaching. Additionally, the knowledge gaps and future directions of AI in medical education, especially anatomy education, are also discussed. With the present advances in AI technologies, their application in anatomical education is still deficient. This review paper provides an overview of recent tools used in anatomy teaching and learning.

Original languageEnglish
Article number283
JournalEducation Sciences
Volume15
Issue number3
DOIs
Publication statusPublished - Mar 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 by the authors.

ASJC Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Education
  • Physical Therapy, Sports Therapy and Rehabilitation
  • Developmental and Educational Psychology
  • Public Administration
  • Computer Science Applications

Keywords

  • anatomy education
  • artificial intelligence
  • deep learning
  • histology
  • machine learning
  • neural networks
  • radiological anatomy

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