Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.

ABSTRACT: Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and tab

SUBMITTER: Tadesse GA 

PROVIDER: S-EPMC10439178 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature