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.
BT - Current Dermatology Reports DO - 10.1007/s13671-025-00469-9 IS - 1 LA - eng M3 - Research Article N2 -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.
PB - Springer Science and Business Media LLC PY - 2025 EP - 10 T2 - Current Dermatology Reports TI - Applications of Artificial Intelligence for High-Burden, Underserved Skin Diseases in Global Settings: a Review UR - https://link.springer.com/article/10.1007/s13671-025-00469-9 VL - 14 SN - 2162-4933 ER -