Diversity of skin images in medical texts: recommendations for student advocacy in medical education
Open accessAbstract
Foundational academic medical texts facilitate foundational understanding of disease recognition in medical students. Significant underrepresentation of darker skin tones and overrepresentation of lighter skin tones in dermatologic texts, general medical texts, and scientific literature is observed. This compromises the clinical tools of trainees when it comes to darker skin tones. Text publishers and editors are steadily beginning to address these disparities, but bottom-up change from trainees is necessary to comprehensively address this issue. In this article the authors propose institutional review panels as a framework for building awareness of underrepresentation of darker skin tones and ensuring that faculty intentionally share diverse presentations in didactics. They also propose trainee engagement in building diverse medical image libraries and including texts on skin of color in institutional libraries. Empowering trainees to be advocates and call out any implicit or explicit biases in image selection can engender change in this area of medical education.
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The lack of large, curated imaging repositories often hinders the full human potential in medical image diagnosis.
“The lack of large, curated imaging repositories often hinders the full human potential in medical image diagnosis.9”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [9]
Medical students might only see a few examples per disease, making it difficult to recognise all cases in practice where there is a large variety in disease presentation and patient morphology.
“Medical students might only see a few examples per disease, making it difficult to recognise all cases in practice where there is a large variety in disease presentation and patient morphology.”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [9]
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References 11 · Cited by 43
- Authors
- Kaundinya, T., & Kundu, R. V.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Kaundinya, Trisha
- Venue
- Journal of Medical Education and Curricular Development
- Publisher
- SAGE Publications
- Published
- 2021-01
- Volume
- 8
- Pages
- 23821205211025855-23821205211025855
- ISSN
- 2382-1205 (ISSN-L)
- PMC
- PMC8202324
- OpenAlex
- W3172677226
- Cited by
- 43
- References
- 11
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 14 · 2023: 5 · 2022: 4
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Medical Education and Curricular Development (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Social Sciences › Sociology and Political Science › Cultural Competency in Health Care
- Keywords
- skin of color, student advocacy
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