biostudies-literatureTadesse GAU.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Science Foundation (NSF)National Science Foundation151https://www.ebi.ac.uk/biostudies/studies/S-EPMC10439178biostudies-literatureUnknown6(1)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 tabNPJ digital medicineSkin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.PMC10439178NSF CAREER 1942926T32 5T32AR007422-38Lipoff JBZou JCintas CAdelekun ASpeakman SVarshney KRJia JTadesse GARotemberg VAgunwa COnyekaba GLester JCDaneshjou RStaar PBailey EEfalseSkin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.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 tab2023-01-01T00:00:00Z2023 Aug2025-04-21T22:10:54.92Z2024-11-09T09:59:37.951ZS-EPMC104391783759632410.1038/s41746-023-00881-0