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March 12, 2026Journal of the American Medical Informatics Association0 citations

Fairness aware subset selection for advancing equity in skin cancer detection

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YPYehuda PerryAAAbdulaziz A AlmuzainiAAAdewole S Adamson

Key Points

  • The aim is to enhance fairness and accuracy in AI-driven skin cancer detection.
  • Developed FAIR-SCAN for subset selection in skin cancer data.
  • Focused on equitable data representation.
  • Evaluated the impact of selection strategies on diagnostic accuracy.
  • Demonstrated improved fairness in diagnostic outcomes.
  • Increased detection accuracy across diverse populations.

Abstract

Strategic data selection is critical for equitable AI-driven diagnostics. FAIR-SCAN advances fairness and accuracy in skin cancer detection, supporting development of trustworthy clinical AI systems.

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Cite This Study

Perry et al. (2026) studied this question.

synapsesocial.com/papers/69b25abe96eeacc4fcec8bcbhttps://doi.org/10.1093/jamia/ocag028
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