@article{103500,
  keywords = {Artificial Intelligence, Clinical decision support tool, frontline health workers, Mobile health, Skin neglected tropical diseases},
  author = {Pons J and Romero-Lopez A and Muñoz I and Carrion C and Fuster-Casanovas A and Lemaire J and Vaquero F and Garcia M and Anwar S and Hsu C and Essmann S and Wilder-Smith A and Mule C and Ruiz-Postigo J},
  title = {The World Health Organization Skin Neglected Tropical Diseases App: A dynamic capacity building training tool enhanced with artificial intelligence.},
  abstract = {<p>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.</p>
},
  year = {2026},
  journal = {The Journal of investigative dermatology},
  pages = {1 - 36},
  month = {05/2026},
  issn = {1523-1747},
  url = {https://www.jidonline.org/action/showPdf?pii=S0022-202X%2826%2901185-1},
  doi = {10.1016/j.jid.2026.03.043},
  language = {ENG},
}