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.
Authors
- Adrià Romero-López (ORCID: https://orcid.org/0000-0001-7173-3680)
- Jeannine Lemaire
- Jordi Serrano Pons (ORCID: https://orcid.org/0009-0007-6763-7750)
- José A Ruiz-Postigo (ORCID: https://orcid.org/0000-0002-7165-8358)
- Caroline Mutio Mule
- Annika B. Wilder-Smith (ORCID: https://orcid.org/0009-0005-2304-0747)
- Montse Garcia (ORCID: https://orcid.org/0009-0002-6560-414X)
- Sarah Anwar (ORCID: https://orcid.org/0000-0001-6683-0048)
- Carme Carrion (ORCID: https://orcid.org/0000-0003-1479-7551)
- Iago Muñoz (ORCID: https://orcid.org/0009-0005-0294-1212)
- Fernando Vaquero (ORCID: https://orcid.org/0009-0008-0243-2882)
- Aïna Fuster-Casanovas (ORCID: https://orcid.org/0000-0001-9128-9580)
- Sofía Paz Villagrán Essmann (ORCID: https://orcid.org/0009-0004-5733-9889)
- Christophe Hsu (ORCID: https://orcid.org/0009-0001-2062-4510)
Institutions
- 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)
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
- 0.00