Abstract：
Previous research suggests that underrepresentation in medical curricula perpetuates inequities in healthcare. This study aimed to quantify the prevalence of human phenotypic diversity (e.g., skin tone, sex, body size, and age) across 11 commonly used anatomy atlases and textbooks in pre-clerkship medical education, published from 2015 to 2020. A systematic visual content analysis was conducted on 5001 images in which at least one phenotypic attribute was quantifiable. Anatomy images most prevalently portrayed light skin tones, males, persons with intermediate body sizes, and young to middle-aged adults. Of the 3883 images in which there was a codable skin tone, 81.2% (n = 3154) depicted light, 14.3% (n = 554) depicted intermediate, and 4.5% (n = 175) depicted dark skin tones. Of the 2384 images that could be categorized into a sex binary, 38.4% (n = 915) depicted females and 61.6% (n = 1469) depicted males. A male bias persisted across all whole-body and regional-body images, including those showing sex organs or those showing characteristics commonly associated with a specific sex (e.g. for males, facial hair and/or muscle hypertrophy). Within sex-specific contexts, darker skin was underrepresented, but male depictions displayed greater overall skin tone variation. Although most images could not be assigned to a body size or age category, when codable, these images overwhelmingly depicted adults (85.0%; 482 of 567) with smaller (34.7%; 93 of 268) or intermediate (64.6%; 173 of 268) body sizes. Ultimately, these outcomes provide reference metrics for monitoring ongoing and future efforts to address representation inequalities portrayed in anatomical imagery.
摘要：
先前的研究表明，医学课程中代表性不足会使医疗保健中的不平等现象长期存在。这项研究旨在量化人类表型多样性的患病率(例如，肤色,性别,身体尺寸,和年龄）跨越11个常用的解剖学地图集和教科书，从2015年到2020年出版。对5001张图像进行了系统的视觉内容分析，其中至少一种表型属性是可量化的。解剖学图像最普遍地描绘了浅肤色，男性,中等体型的人，以及年轻人到中年人。在3883张具有可编码肤色的图像中，81.2%(n=3154)描绘光，14.3%(n=554)描述的中间体，4.5%(n=175)描绘深色肤色。在2384张可以归类为性别二元的图像中，38.4%(n=915)描述女性,61.6%(n=1469)描述男性。男性偏见持续存在于所有全身和区域身体图像中，包括那些显示性器官的人或那些显示通常与特定性别相关的特征的人(例如，对于男性，面部毛发和/或肌肉肥大)。在特定性别的背景下，较深的皮肤代表不足，但是男性的描述显示出更大的整体肤色变化。虽然大多数图像不能被分配到一个身体大小或年龄类别，当可编码时，这些图像绝大多数描绘了较小(34.7%;93/268)或中等(64.6%;173/268)体型的成年人(85.0%;567中的482人)。最终,这些结果为监测正在进行的和未来的工作提供了参考指标,以解决解剖学图像中描绘的表示不平等.
