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Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
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Anatomical Fidelity in Text-to-Image Generative AI: A Comparative Qualitative and Quantitative Evaluation.

Emad A Albadawi1, Eman M Embaby2, Khalid I AlQumaizi3

  • 1Department of Basic Medical Sciences, College of Medicine, Taibah University, Madinah, Saudi Arabia.

Clinical Anatomy (New York, N.Y.)
|July 4, 2026
PubMed
Summary

Generative artificial intelligence (AI) image generators offer potential for medical illustration but vary in accuracy. Gemini and Copilot show promise for education, though expert oversight is crucial to prevent misinformation from inaccurate AI-generated anatomical images.

Keywords:
Geminianatomical educationcopilotgenerative AImedical illustration

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Anatomical Illustration

Background:

  • Traditional anatomical resources (cadavers, atlases) have cost and accessibility limitations.
  • Generative AI offers a cost-effective, rapid alternative for custom anatomical illustrations.
  • The anatomical accuracy and educational reliability of AI models require validation.

Purpose of the Study:

  • To evaluate the anatomical accuracy of four prominent text-to-image AI generators.
  • To assess AI-generated illustrations of the human heart, kidney, and brain.
  • To determine the reliability and educational utility of AI in anatomical representation.

Main Methods:

  • Four AI platforms (Copilot, Microsoft Designer, Gemini, Freepik) generated heart, kidney, and brain illustrations.
  • A radiologist and two anatomists evaluated 12 images using a 5-point Likert scale.
  • Evaluation domains included textural realism, topological validity, morphometric accuracy, and educational utility.

Main Results:

  • Significant performance variability observed across AI platforms.
  • Gemini demonstrated superior performance, especially in complex topological tasks.
  • Copilot showed high spatial accuracy for specific anatomical features (e.g., renal hilum).
  • Accuracy decreased with increasing anatomical complexity; some AI outputs were biologically impossible.

Conclusions:

  • AI-generated anatomical illustrations range from accurate to highly inaccurate.
  • Gemini and Copilot show potential for supplemental medical education.
  • Expert supervision and AI models trained on curated medical data are essential to mitigate risks of misinformation.