Haider Syed Ali, Prabha Srinivasagam, Gomez-Cabello Cesar A, Borna Sahar, Pressman Sophia M, Genovese Ariana, Trabilsy Maissa, Galvao Andrea, Aziz Keith T, Murray Peter M, Parte Yogesh, Yu Yunguo, Tao Cui, Forte Antonio Jorge
Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
School of Dental, Unichristus, Fortaleza 60190-180, Brazil.
J Clin Med. 2025 Mar 21;14(7):2136. doi: 10.3390/jcm14072136.
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, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. These findings highlight GAI's potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use.
在医学教育中，解剖学精确的插图至关重要，是帮助理解复杂解剖结构的关键工具。虽然涉及人类艺术家的传统插图方法仍是黄金标准，但生成式人工智能（GAI）模型的迅速发展为自动化和加速这一过程带来了新机遇。本研究评估了GAI模型用于生成用于教育目的的颅面解剖学插图的潜力。使用了四个GAI模型，包括Midjourney v6.0、DALL-E 3、Gemini Ultra 1.0和Stable Diffusion 2.0，以油画和写实照片风格生成了736张涵盖颅骨表面解剖、骨骼、肌肉、血管和神经多个视图的图像。四名评审员对图像的解剖细节、美学质量、可用性和成本效益进行了评估。评分者间信度分析评估了评估的一致性。Midjourney v6.0在美学质量和成本效益方面得分最高，DALL-E 3在解剖细节和可用性方面表现最佳。评分者间信度分析表明评审员之间具有高度一致性（ICC = 0.858，95% CI）。然而，所有模型在描绘关键解剖细节（如孔、缝线、肌肉起点/止点和神经血管结构）时都存在明显缺陷。这些局限性进一步表现为抽象描绘、层次混合、阴影、异常肌肉排列和标注错误。这些发现凸显了GAI在快速创建颅面解剖学插图方面的潜力，但也指出了其由于训练数据不足和对复杂解剖结构理解不完整而存在的当前局限性。通过精确的训练数据和专家反馈来完善这些模型至关重要。还必须谨慎应对伦理考量，如潜在偏见、版权挑战以及传播不准确信息的风险。进一步完善GAI模型和伦理保障措施对于安全使用至关重要。
