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Disparities in the diagnosis of erythema across different skin tones present a significant barrier to equitable dermatologic care. These disparities arise from a focus on lighter skin tones in clinical training and diagnostic criteria, leading to challenges in accurately identifying erythema in individuals with Fitzpatrick skin types IV to VI. Redness associated with vasodilation may not be visibly apparent or can manifest differently on darker skin, resulting in underdiagnosis or misclassification of inflammatory skin conditions. Current assessment tools, like the Investigator's Global Assessment (IGA) and the Eczema Area and Severity Index (EASI), emphasize visible erythema and lack validated alternatives for variations in pigmentation. To promote diagnostic equity, there is a need to update inflammatory assessment methods by integrating objective metrics such as high-frequency ultrasound and infrared imaging, while also developing new training protocols that include diverse skin representations. By redefining erythema with inclusive terminology and incorporating diverse datasets in machine-learning tools, we can enhance diagnostic accuracy and ensure equitable care in inflammatory dermatology for all populations.
Forsyth et al. (Sun,) studied this question.