Skin 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 table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96 ± 0.02 AUROC and 0.90 ± 0.06 F1 score) and classifying skin tones (0.87 ± 0.01 AUROC and 0.91 ± 0.00 F1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.

Recognizing Racism in Medicine: A Student-Organized and Community-Engaged Health Professional Conference.

Purpose: This piece details the evaluation and implementation of a student-led educational intervention designed to train health professionals on the impact of racism in health care and provide tools to mitigate it. In addition, this conference, cosponsored by medical, nursing, and social work training programs, facilitates development of networks of providers with the knowledge and skills to recognize and address racism in health care. Methods: The conference included 2 keynote speakers, an interprofessional panel, and 15 workshops. Participants (n=220) were asked to complete a survey assessing perceptions of conference content and impact. We compared responses pre- and postconference using Wilcoxon signed-rank tests. Results: Of the survey respondents (n=44), 45.5% were medical students, 13.6% nursing students, and 9% social work students; 65.9% self-identified as a race/ethnicity other than non-Hispanic white; and 63.6% self-identified as female. We found that 47.7% respondents reported they were more comfortable discussing how racism affects health (p<0.001), 36.4% had better understanding of the impact of racism on an individual's health (p<0.001), and 54.5% felt more connected to other health professionals working to recognize and address racism in medicine (p<0.001). Conclusion: These findings suggest that a student-organized conference could potentially be an effective strategy in addressing a critical gap in racism training for health care professionals.