Standardizing Pigment-Specific Dermoscopic and AIBased Diagnostic Tools to Address Racial Disparities in Dermatology

¹Department of Medicine, Piedmont Healthcare, Macon, GA, USA
²Department of Pediatrics, University of North Carolina - Chapel Hill, NC, USA
³Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
⁴Department of Dermatology, Northwell Health, New Hyde Park, NY, USA

Corresponding author: Kelly Frasier, DO, MS, Department of Dermatology, Northwell Health, New Hyde Park, NY, United States, Phone: 3105956882, Email: [email protected]

Received Date: September 15, 2025
Published Date: October 13, 2025

Citation: Rabeeah Z, et al. (2025). Standardizing Pigment-Specific Dermoscopic and AI-Based Diagnostic Tools to Address
Racial Disparities in Dermatology. Dermis. 5(6):54.

Copyright: Rabeeah Z, et al. © (2026).

 ABSTRACT

Standardizing pigment-specific dermoscopic and AI-based diagnostic tools offers a transformative approach to addressing racial disparities in dermatology, particularly in the accurate diagnosis and management of pigmentary skin conditions. Traditional dermatological diagnostic methods, which often rely on visual assessments, are frequently less effective for individuals with darker skin tones due to the challenges posed by pigmentation variations, making it difficult to detect subtle changes in lesions that may indicate malignancy or progression. Advanced dermoscopy techniques, specifically designed to identify pigmentary changes in skin of color, are now integrated with artificial intelligence, enhancing both diagnostic precision and efficiency. AI-driven algorithms, trained on large, diverse datasets that accurately reflect a range of skin tones, enable superior lesion detection, classification, and risk stratification, particularly for conditions such as melanoma, basal cell carcinoma, and other pigmented lesions. The integration of high-resolution dermoscopic imaging with AI tools allows for the identification of features that might otherwise remain undetected, such as nuanced changes in color, shape, and texture of lesions in darker skin. Standardizing these technologies ensures their consistent, reliable application across clinical environments, making them effective for diverse patient populations. By incorporating pigment-specific dermoscopy and AI into dermatology practice, healthcare providers can significantly enhance diagnostic accuracy, reduce the incidence of misdiagnoses, and address the challenge of late-stage diagnoses, which are disproportionately prevalent in racially diverse populations. Integrating AI-based tools into medical education and clinical practice supports more equitable care, equipping dermatologists with the decision-making capabilities needed to provide optimal, individualized treatment, and ultimately reducing health disparities within dermatology.

Keywords: Skin of Color, Dermatology, Artificial Intelligence, Dermatologic Care.