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Cited by 120Anatomy and Medical TechnologyArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

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Abdellatif, Hussein, Al Mushaiqri, Mohamed, Albalushi, Halima, Al-Zaabi, Adhari Abdullah, Roychoudhury, Sadhana, Das, Srijit·2022·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph192114209PMC PMC9656803
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Abstract

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

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  • The higher retention rate observed in the AI-Enhanced Group suggests that AI-supported learning environments may facilitate deeper cognitive processing and more durable memory formation.

    “The higher retention rate observed in the AI-Enhanced Group suggests that AI-supported learning environments may facilitate deeper cognitive processing and more durable memory formation [27].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [27]

    Section 3 reports that AI-assisted learning has improved learners’ knowledge retention, and the Conclusions describe AI tools as a resource for deep and logical learning (page numbers unavailable). The paper does not report a comparison involving an “AI-Enhanced Group” or establish that deeper cognitive processing explains a higher retention rate. (LLM, conf=0.85)

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  • Several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods, as the costs of servers, faculty training, tool development, and technical personnel could be substantial.

    “Finally, several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods [9, 50, 181], as the costs of servers, faculty training, tool development, and technical personnel could be substantial [182–184].”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [182]

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Research works printed but not linked: 1

  1. [31]McCarthy J. Minsky M.L. Rochester N. Shannon C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 AI Magazine 31 December 2016 Available online: https://ojs.aaai.org//index.php/aimagazine/article/view/1904 (accessed on 25 August 2022)

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Authors
Abdellatif, H., Al Mushaiqri, M., Albalushi, H., Al-Zaabi, A. A., Roychoudhury, S., & Das, S.
Affiliations
  • Sultan Qaboos University (OM)
  • Department of Human & Clinical Anatomy, College of Medicine & Health Sciences, Sultan Qaboos University, Al Khoud, Muscat 123, Oman
Corresponding author
Das, Srijit
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2022-10-31
Volume
19
Issue
21
Pages
14209
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph192114209
PMC
PMC9656803
OpenAlex
W4308118441
Cited by
120
References
72
Citations by year
2026: 38 · 2025: 38 · 2024: 25 · 2023: 18 · 2022: 1
FWCI
1.83
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence in medical education, anatomy education, artificial neural networks, convolutional neural networks, anatomy assessment, augmented reality, COVID-19 pandemic, cadaver shortage, neural pathways, embryonic development, inner ear, deep learning
MeSH terms
COVID-19, Artificial Intelligence, Bayes Theorem, Curriculum, Humans, Teaching, Pandemics

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