Artificial intelligence in dermatology: advancements and challenges in skin of color
Clinical Summary
View sourceWhat was studied
A literature review examined artificial intelligence tools used in dermatology for patients with skin of color, using PubMed and Google Scholar searches from February 2002 to June 2023 to assess representation, performance, and implementation challenges.
Key findings
Across 10 studies covering 15 AI technologies, only 30% of identified programs had dermatology data reported specifically in skin of color; several algorithms performed worse on darker skin (e.g., ROC-AUC: ModelDerm 0.55 vs 0.64, DeepDerm 0.50 vs 0.61, HAM10000 0.57 vs 0.72 for FST V–VI vs I–II).
Study limitations
The program lists are explicitly nonexhaustive and the search included only English-language publications; many included studies lacked standardized metadata (e.g., skin tone and lesion location per CLEAR Derm), limiting SOC-specific conclusions.
Clinical implications
Use current dermatology AI tools cautiously for patients with darker skin, as many lack SOC validation and show reduced accuracy; when obtaining images, apply consistent, standardized photography and include key metadata (skin tone, lesion location) to support equitable AI use.
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