Machine Learning and Health Care Disparities in Dermatology

Machine learning (ML), a form of artificial intelligence using computer algorithms, is often applied in ways we take for granted: Spotify to predict music that people may enjoy, Facebook to suggest friends to tag in photos, and Amazon to identify products to buy. Aside from these quotidian tasks, ML holds the promise of enhancing the delivery of quality healthcare. Recently, ML has been used to create programs capable of distinguishing between images of benign and malignant moles with accuracy similar to that of board-certified dermatologists. This technology could greatly assist dermatologists in diagnosing and treating skin diseases, thereby improving patient care. However, if not developed with inclusivity in mind, ML could exacerbate health care disparities in dermatology.

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Publication Details

Journal
UNC Libraries
Published
2025-08-29
DOI
https://doi.org/10.17615/q5dr-ss14
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning and Health Care Disparities in Dermatology

Adewole S. Adamson, Avery Smith
UNC Libraries
Cutaneous Melanoma Detection and Management
article

Machine Learning and Health Care Disparities in Dermatology

Adewole S. Adamson, Avery Smith
article en

Abstract

Machine learning (ML), a form of artificial intelligence using computer algorithms, is often applied in ways we take for granted: Spotify to predict music that people may enjoy, Facebook to suggest friends to tag in photos, and Amazon to identify products to buy. Aside from these quotidian tasks, ML holds the promise of enhancing the delivery of quality healthcare. Recently, ML has been used to create programs capable of distinguishing between images of benign and malignant moles with accuracy similar to that of board-certified dermatologists. This technology could greatly assist dermatologists in diagnosing and treating skin diseases, thereby improving patient care. However, if not developed with inclusivity in mind, ML could exacerbate health care disparities in dermatology.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 65%
Cutaneous Melanoma Detection and Management
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