02038nas a2200253   4500000000100000008004100001260004400042653002100086653001800107653003700125653002900162653002300191653001800214653003700232653002900269100001200298700001400310245011600324856006500440300000700505490000700512520125100519022001401770       2025                            d  bSpringer Science and Business Media LLC10amachine learning10aDeep learning10aSkin neglected tropical diseases10ainfectious skin diseases10aglobal dermatology10aGlobal health10aLow- and middle-income countries10aUnder-resourced settings1 aSales C1 aCoates SJ00aApplications of Artificial Intelligence for High-Burden, Underserved Skin Diseases in Global Settings: a Review  uhttps://link.springer.com/article/10.1007/s13671-025-00469-9  a100 v143 a<p><strong>Purpose&nbsp;of Review </strong>To examine current evidence on the applications of artificial intelligence&nbsp;(AI) for high-burden, underserved dermatologic diseases in low-resource global communities.</p>

<p><strong>Recent Findings</strong> Artificial intelligence has emerged as a potential solution to expedite and increase access to dermatologic care. In dermatology, the most common application of artificial intelligence tools is diagnostic assistance. However, recent studies have shown the potential of AI-based tools to guide personalized treatment, enhance provider learning, and refine public health interventions through predictive modeling.</p>

<p><strong>Summary</strong> Several challenges hinder the robust and responsible development of artificial intelligence tools for dermatology practiced in low-resource global settings. Training datasets should be ethically obtained, biopsy proven when possible, and accurately represent real-world clinical settings and diverse skin tones. Tools should be available at low cost and compatible with or tailored to local contexts, needs, and capacities. These changes could potentially improve the accessibility and accuracy of future artificial intelligence tools.</p>
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