A Validity Analysis of Text-to-Image Generative Artificial Intelligence Models for Craniofacial Anatomy Illustration DOI Open Access
Syed Ali Haider,

Srinivasagam Prabha,

Cesar A. Gomez-Cabello

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2136 - 2136

Published: March 21, 2025

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, rapid advancement Generative Artificial Intelligence (GAI) models presents a new opportunity automate and accelerate this process. This study evaluated potential GAI produce craniofacial anatomy for educational purposes. Methods: Four models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, Stable Diffusion 2.0 were used generate 736 images across multiple views surface anatomy, bones, muscles, blood vessels, nerves cranium both oil painting realistic photograph styles. reviewers detail, aesthetic quality, usability, cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: v6.0 scored highest quality cost-effectiveness, 3 performed best detail usability. The inter-rater demonstrated high level agreement among (ICC = 0.858, 95% CI). However, all showed significant flaws depicting details such foramina, suture lines, muscular origins/insertions, neurovascular These limitations further characterized by abstract depictions, mixing layers, shadowing, abnormal muscle arrangements, labeling errors. Conclusions: findings highlight GAI's rapidly creating but also its current due inadequate training data incomplete understanding anatomy. Refining these through precise expert feedback is vital. Ethical considerations, biases, copyright challenges, risks propagating inaccurate information, must be carefully navigated. Further refinement ethical safeguards essential safe use.

Language: Английский

A Validity Analysis of Text-to-Image Generative Artificial Intelligence Models for Craniofacial Anatomy Illustration DOI Open Access
Syed Ali Haider,

Srinivasagam Prabha,

Cesar A. Gomez-Cabello

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(7), P. 2136 - 2136

Published: March 21, 2025

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, rapid advancement Generative Artificial Intelligence (GAI) models presents a new opportunity automate and accelerate this process. This study evaluated potential GAI produce craniofacial anatomy for educational purposes. Methods: Four models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, Stable Diffusion 2.0 were used generate 736 images across multiple views surface anatomy, bones, muscles, blood vessels, nerves cranium both oil painting realistic photograph styles. reviewers detail, aesthetic quality, usability, cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: v6.0 scored highest quality cost-effectiveness, 3 performed best detail usability. The inter-rater demonstrated high level agreement among (ICC = 0.858, 95% CI). However, all showed significant flaws depicting details such foramina, suture lines, muscular origins/insertions, neurovascular These limitations further characterized by abstract depictions, mixing layers, shadowing, abnormal muscle arrangements, labeling errors. Conclusions: findings highlight GAI's rapidly creating but also its current due inadequate training data incomplete understanding anatomy. Refining these through precise expert feedback is vital. Ethical considerations, biases, copyright challenges, risks propagating inaccurate information, must be carefully navigated. Further refinement ethical safeguards essential safe use.

Language: Английский

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