The World Health Organization Skin Neglected Tropical Diseases App: A dynamic capacity building training tool enhanced with artificial intelligence

Skin neglected tropical diseases (NTDs) remain a major public health challenge in low- and middle-income countries, where frontline health workers (FHWs) often lack dermatological training. In response, the World Health Organization (WHO) created the Skin NTDs App-developed by UniversalDoctor-to support FHWs in resource-limited settings. Initially created as a digital adaptation of a WHO's training guide, the App evolved by incorporating another clinical decision support tool (CDST) from until No Leprosy Remains and an artificial intelligence (AI)-powered visual classifier (VC). Our purpose is to describe the WHO Skin NTDs App and evaluate its AI-powered VC. The VC was trained to identify 12 skin NTDs out of 13 through a convolutional neural network (DenseNet-121). Performance was assessed through sensitivity, specificity, accuracy, precision, F1-score, and per-class sensitivity from top-1 through top-5. The VC demonstrated high performance in internal evaluations, achieving 99.8% top-5 sensitivity across all diseases and top-1 accuracy above 75% for most diseases. Some underrepresented conditions (e.g., chromoblastomycosis, sporotrichosis) showed lower precision. In conclusion, the AI-powered WHO Skin NTDs App is a promising digital tool for capacity-building of FHW in underserved areas. Continued development, external validation, and integration into clinical workflows will be critical to assess its performance globally.

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Publication Details

Journal
Journal of Investigative Dermatology
Published
2026-05-01
DOI
https://doi.org/10.1016/j.jid.2026.03.043
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

The World Health Organization Skin Neglected Tropical Diseases App: A dynamic capacity building training tool enhanced with artificial intelligence

Adrià Romero-López, Jeannine Lemaire, Jordi Serrano Pons, José A Ruiz-Postigo et al.
Journal of Investigative Dermatology
Artificial Intelligence in Healthcare and Education
article

The World Health Organization Skin Neglected Tropical Diseases App: A dynamic capacity building training tool enhanced with artificial intelligence

Adrià Romero-López, Jeannine Lemaire, Jordi Serrano Pons, José A Ruiz-Postigo, Caroline Mutio Mule, Annika B. Wilder-Smith, Montse Garcia, Sarah Anwar, Carme Carrion, Iago Muñoz, Fernando Vaquero, Aïna Fuster-Casanovas, Sofía Paz Villagrán Essmann, Christophe Hsu
article en

Abstract

Skin neglected tropical diseases (NTDs) remain a major public health challenge in low- and middle-income countries, where frontline health workers (FHWs) often lack dermatological training. In response, the World Health Organization (WHO) created the Skin NTDs App-developed by UniversalDoctor-to support FHWs in resource-limited settings. Initially created as a digital adaptation of a WHO's training guide, the App evolved by incorporating another clinical decision support tool (CDST) from until No Leprosy Remains and an artificial intelligence (AI)-powered visual classifier (VC). Our purpose is to describe the WHO Skin NTDs App and evaluate its AI-powered VC. The VC was trained to identify 12 skin NTDs out of 13 through a convolutional neural network (DenseNet-121). Performance was assessed through sensitivity, specificity, accuracy, precision, F1-score, and per-class sensitivity from top-1 through top-5. The VC demonstrated high performance in internal evaluations, achieving 99.8% top-5 sensitivity across all diseases and top-1 accuracy above 75% for most diseases. Some underrepresented conditions (e.g., chromoblastomycosis, sporotrichosis) showed lower precision. In conclusion, the AI-powered WHO Skin NTDs App is a promising digital tool for capacity-building of FHW in underserved areas. Continued development, external validation, and integration into clinical workflows will be critical to assess its performance globally.

Journal of Investigative Dermatology
Tufts University (US), Universitat Oberta de Catalunya (ES), Pontificia Universidad Católica de Chile (CL), ZHAW Zurich University of Applied Sciences (CH), Jomo Kenyatta University of Agriculture and Technology (KE), Kenyatta National Hospital (KE), Massachusetts General Hospital (US), International League of Dermatological Societies (GB), World Health Organization (CH), Institute of Dermatology (TH)
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
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