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

Girmaw Abebe Tadesse1, Celia Cintas2, Kush R Varshney3

  • 1IBM Research - Africa, Nairobi, Kenya. girmawabebe@gmail.com.

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|August 18, 2023
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Summary
This summary is machine-generated.

Medical education materials lack diverse skin tones, potentially causing diagnostic disparities. A new AI tool, STAR-ED (Skin Tone Analysis for Representation in EDucational materials), automates skin tone assessment in textbooks, revealing significant underrepresentation of darker skin tones.

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Area of Science:

  • Medical education
  • Dermatology
  • Machine Learning

Background:

  • Images of dark skin tones are underrepresented in medical education materials.
  • This underrepresentation may contribute to disparities in diagnosing skin diseases across racial groups.
  • Manual assessment of image diversity is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and validate an automated framework for assessing skin tone representation in medical educational materials.
  • To quantify the extent of skin tone imbalance in commonly used medical textbooks.
  • To provide a tool for improving diversity in medical education content.

Main Methods:

  • The Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework was developed using machine learning.
  • STAR-ED processes documents to extract text, images, and tables.
  • It identifies skin in images, segments skin areas, and estimates skin tone using the Fitzpatrick17k dataset.

Main Results:

  • STAR-ED demonstrated high performance in detecting skin images (0.96 AUROC) and classifying skin tones (0.87 AUROC).
  • External testing on four medical textbooks revealed that images of brown and black skin tones (Fitzpatrick V-VI) comprised only 10.5% of all skin images.
  • The framework successfully quantified the significant imbalance in skin tone representation.

Conclusions:

  • STAR-ED provides an automated and scalable solution for evaluating skin tone diversity in medical education.
  • The study highlights a critical lack of representation for darker skin tones in current medical textbooks.
  • This technology can aid educators, publishers, and clinicians in creating more inclusive and equitable medical learning resources.