Barriers to AI-Enabled Mole Mapping: The Impact of Socioeconomic, Racial, Geographic, Insurance, and Tattoo-Related Factors on Early Detection

¹Indiana University School of Medicine, Indianapolis, IN, USA

²Lincoln Memorial University DeBusk College of Osteopathic Medicine, Harrogate, TN, USA

³University of Kentucky College of Medicine, Bowling Green, KY, USA

⁴Toby School of Medicine, St. George's University, Islip, NY, USA

⁵Lake Erie College of Osteopathic Medicine, Bradenton, FL, USA

⁶American University of Antigua College of Medicine and MCPHS, USA

⁷Department 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 17, 2025

Publication Date: December 02, 2025

Citation: Bell K, et al. (2025). Barriers to AI-Enabled Mole Mapping: The Impact of Socioeconomic, Racial, Geographic, Insurance, and Tattoo-Related Factors on Early Detection. Dermis. 5(6):53.

Copyright: Bell K, et al. © (2025).

ABSTRACT

Background: Digital mole mapping, integrating total body photography and lesion tracking, is increasingly used for melanoma surveillance. Artificial Intelligence (AI) integration with these technologies offers to enhance identification of worrisome lesions and provide diagnostic support. Yet, its real-world application may be influenced by many socioeconomic, racial, geographic, insurance, and tattoo-related factors that could limit equitable uptake. Methods: A PRISMA-guided systematic review was performed to identify barriers to AI-enabled mole mapping for melanoma detection. PubMed and Embase were searched for English-language studies published from Jan 2018 to August 2-25, combining AI, mole-mapping, and barrier-related terms. Eligible studies included original research, systematic reviews, or meta-analyses reporting at least one prespecified barrier such as socioeconomic status, race or ethnicity, skin color, geography, insurance status, or tattoos. Case reports, abstracts, and non-AI imaging studies were excluded. Two reviewers independently screened articles. Of 16 records identified, five met inclusion criteria. Results: Key barriers included cost, geographic inaccessibility particularly in rural settings, privacy concerns, and patient mistrust of AI. Underrepresentation of darker skin phototypes in training datasets reduced diagnostic accuracy for patients with skin of color. Tattoos were noted as potential confounders for lesion recognition, although no AI-specific studies addressed this factor. Similarly, while socioeconomic and insurance disparities are recognized in dermatology, no study directly examined insurance barriers in the context of AI mole mapping. Patients expressed support for AI as an adjunct ot physician but voiced concerns regarding data protection and the loss of human interaction. Clinicians emphasized AI’s prime for triage and efficiency but stressed the need for oversight. Conclusion: AI-assisted mole mapping may enhance melanoma detection and patient reassurance. However, barriers related to equity, dataset diversity, and evidence gaps regarding insurance and tattoo-related barriers must be addressed to ensure safe and effective integration of AI-assisted mole mapping technologies.

Keywords: Artificial Intelligence, Dermatology, Mole Mapping, Skin Cancer, Mental Health, Geographic Location, Skin Color