Demographic Reporting and the Absence of Hispanic/Latino Representation in Dermatology Artificial Intelligence: A Scoping Review.

BACKGROUND

Artificial intelligence (AI) is increasingly integrated into dermatology, particularly through image-based diagnostic tools. However, concerns persist regarding unequal performance across skin tones and limited demographic transparency in model development. Evidence suggests that AI systems trained predominantly on lighter skin types may perform less accurately in darker phototypes, yet reporting of skin tone, race, and ethnicity remains inconsistent across studies.

OBJECTIVE

To map how dermatology AI studies report skin tone, race and ethnicity, Hispanic/Latino identity, and subgroup diagnostic performance.

METHODS

PubMed, Embase, and Scopus were searched for articles published between January 2020 and November 2025 that applied AI or deep learning to human dermatologic conditions and reported at least one diagnostic performance metric. Two reviewers independently screened studies and extracted data on demographic reporting, dataset composition, and performance across subgroups.

RESULTS

Sixteen studies met inclusion criteria. Most datasets were composed predominantly of lighter skin types (Fitzpatrick I-III), with limited inclusion of Fitzpatrick V-VI. Although thirteen studies reported some form of skin-tone information, reporting approaches varied substantially. Race and ethnicity were inconsistently documented, often using broad categories, and no study explicitly identified Hispanic or Latino participants. Only a minority of studies stratified diagnostic performance by skin tone or demographic subgroups; Among those that performed subgroup analyses, consistent performance disparities were observed, including reduced specificity, increased false-positive rates, and lower overall diagnostic accuracy in darker skin tones.

CONCLUSIONS

Current dermatology AI literature frequently lacks the demographic transparency necessary to assess algorithmic fairness and generalizability. The underrepresentation of darker skin tones and the absence of Hispanic/Latino identity reporting limit the equitable clinical applicability of existing AI systems. Standardized demographic reporting, intentional dataset diversification, and routine subgroup performance analyses are essential for the development of fair and inclusive dermatology AI tools.