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Purpose&#xa0;of Review <p>To examine current evidence on the applications of artificial intelligence&#xa0;(AI) for high-burden, underserved dermatologic diseases in low-resource global communities.</p> Recent Findings <p>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> Summary <p>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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Applications of Artificial Intelligence for High-Burden, Underserved Skin Diseases in Global Settings: a Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

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  • Chloe Sales,
  • Sarah J Coates

摘要

Purpose of Review

To examine current evidence on the applications of artificial intelligence (AI) for high-burden, underserved dermatologic diseases in low-resource global communities.

Recent Findings

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.

Summary

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.