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评估人工智能文本到图像生成器在解剖学插图中的应用:一项比较研究。

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

机构信息

Division of Anatomy, Department of Surgery, University of California, San Diego, La Jolla, California, USA.

Division of Anatomical Sciences, Department of Anatomy and Cell Biology, McGill University, Montreal, Québec, Canada.

出版信息

Anat Sci Educ. 2024 Jul-Aug;17(5):979-983. doi: 10.1002/ase.2336. Epub 2023 Sep 11.

Abstract

Medical illustration, which involves the creation of visual representations of anatomy, has long been an essential tool for medical professionals and educators. The integration of AI and medical illustration has the potential to revolutionize the field of anatomy education, providing highly accurate, customizable images. The authors evaluated three AI-powered text-to-image generators in producing anatomical illustrations of the human skulls, heart, and brain. The generators were assessed for their accurate depiction of foramina, suture lines, coronary arteries, aortic and pulmonary trunk branching, gyri, sulci, and the relationship between the cerebellum and temporal lobes. None of the generators produced illustrations with comprehensive anatomical details. Foramina, such as the mental and supraorbital foramina, were frequently omitted, and suture lines were inaccurately represented. The illustrations of the heart failed to indicate proper coronary artery origins, and the branching of the aorta and pulmonary trunk was often incorrect. Brain illustrations lacked accurate gyri and sulci depiction, and the relationship between the cerebellum and temporal lobes remained unclear. Although AI generators tended toward esoteric imagery, they exhibited significant speed and cost advantages over human illustrators. However, improving their accuracy necessitates augmenting the training databases with anatomically correct images. The study emphasizes the ongoing role of human medical illustrators, especially in ensuring the provision of accurate and accessible illustrations.

摘要

医学插图,涉及到对解剖结构的视觉表现的创作,长期以来一直是医学专业人员和教育工作者的重要工具。人工智能与医学插图的结合有可能彻底改变解剖学教育领域,提供高度准确、可定制的图像。作者评估了三种基于人工智能的文本到图像生成器在生成人类头骨、心脏和大脑的解剖插图方面的表现。这些生成器在准确描绘孔、缝线、冠状动脉、主动脉和肺动脉分支、脑回、脑沟以及小脑和颞叶之间的关系方面进行了评估。没有一个生成器能够生成具有全面解剖细节的插图。例如,颏孔和眶上孔等孔经常被忽略,缝线也被不准确地表示。心脏插图未能正确显示冠状动脉的起源,主动脉和肺动脉的分支也经常不正确。大脑插图缺乏准确的脑回和脑沟描绘,小脑和颞叶之间的关系仍然不清楚。尽管人工智能生成器倾向于深奥的图像,但它们在速度和成本方面相对于人类插图画家具有显著优势。然而,提高它们的准确性需要通过增加具有正确解剖结构的图像来扩充训练数据库。该研究强调了人类医学插图画家的持续作用,特别是在确保提供准确和可访问的插图方面。

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