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Related Concept Videos

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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The two main features of a long bone are the diaphysis and the epiphysis.
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Intramembranous ossification is one of the two processes involved in the development of bones within an embryo. The flat bones of the face, most of the cranial bones, and the clavicles are formed via this process. During intramembranous ossification, the bones develop directly from sheets of undifferentiated mesenchymal connective tissue.
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A Comparative Study of Generative Artificial Intelligence Tools for Human Bone Learning.

Wachirawit Sirirat1, Paak Rewthamrongsris1,2, Kritchai Bespinyowong1,2

  • 1Center of Artificial Intelligence and Innovation (CAII) and Centre of Excellence for Dental Stem Cell Biology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.

Clinical Anatomy (New York, N.Y.)
|February 28, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI tools like ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash show low accuracy and consistency for human skeletal anatomy education. These AI models are unreliable for anatomy learning due to frequent inaccuracies.

Keywords:
dental educationdentistrygenerative artificial intelligencehuman anatomyidentification

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Anatomy
  • Digital Learning Tools

Background:

  • Self-directed learning in human skeletal anatomy is crucial for medical students.
  • Generative AI tools offer potential for educational support but require validation.
  • Evaluating AI accuracy and consistency is essential before clinical application.

Purpose of the Study:

  • To assess the effectiveness of ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash in human skeletal anatomy.
  • To determine the accuracy and response consistency of these AI tools for self-directed anatomy learning.
  • To identify the reliability of generative AI for anatomical education.

Main Methods:

  • Evaluated 143 human skeletal specimens with 715 images.
  • Generated 4 question types to test AI identification of anatomical features.
  • Assessed response accuracy (correct, incorrect, could not be specified, not analyzable) and consistency (5 trials on 105 images).

Main Results:

  • ChatGPT-4o achieved the highest accuracy (44.75%), outperforming Claude 3.7 Sonnet and Gemini 2.0 Flash.
  • Agreement between AI models ranged from slight to fair (Cohen's κ).
  • Gemini 2.0 Flash produced 'not analyzable' responses; it had the highest consistency (62.86% identical responses). Claude 3.7 Sonnet showed the most inconsistent responses.

Conclusions:

  • Current generative AI models lack the reliability needed for effective anatomy education.
  • These AI tools exhibit a high propensity for generating inaccurate anatomical information.
  • Caution is advised when using generative AI for self-directed learning in human skeletal anatomy.