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Evaluating AI-powered text-to-image generators for anatomical illustration: A comparative study.

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AI image generators show speed and cost benefits for anatomical illustration but lack accuracy. Further development with correct anatomical data is needed to improve their utility in medical education.

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Area of Science:

  • Anatomical visualization
  • Medical illustration
  • Artificial intelligence in education

Background:

  • Medical illustration is crucial for anatomy education.
  • AI offers potential for customizable and accurate anatomical images.
  • Evaluating AI's current capabilities in anatomical illustration is necessary.

Purpose of the Study:

  • To assess the accuracy of AI text-to-image generators in creating anatomical illustrations.
  • To compare AI-generated images of human skulls, hearts, and brains against anatomical standards.
  • To identify the strengths and weaknesses of AI in medical illustration.

Main Methods:

  • Three AI text-to-image generators were utilized.
  • AI models were prompted to create illustrations of human skulls, hearts, and brains.
  • Generated images were evaluated for anatomical accuracy, focusing on specific structures and relationships.

Main Results:

  • None of the AI generators produced fully accurate anatomical illustrations.
  • Omissions and inaccuracies were noted in depictions of foramina, suture lines, coronary arteries, major vessel branching, and brain structures.
  • AI generators demonstrated speed and cost advantages but often produced esoteric imagery.

Conclusions:

  • Current AI generators are not yet sufficient for comprehensive, accurate anatomical illustration.
  • Enhancing AI training datasets with anatomically correct images is essential for improvement.
  • Human medical illustrators remain vital for ensuring accuracy and accessibility in anatomical education.