02470nas a2200349   4500000000100000008004100001260001200042653002800054653003500082653002900117653001800146653003700164100001100201700001900212700001300231700001400244700002300258700001400281700001400295700001300309700001200322700001000334700001400344700001900358700001100377700001900388245015300407856007500560300001100635520146000646022001402106       2026                            d  c05/202610aArtificial Intelligence10aClinical decision support tool10afrontline health workers10aMobile health10aSkin neglected tropical diseases1 aPons J1 aRomero-Lopez A1 aMuñoz I1 aCarrion C1 aFuster-Casanovas A1 aLemaire J1 aVaquero F1 aGarcia M1 aAnwar S1 aHsu C1 aEssmann S1 aWilder-Smith A1 aMule C1 aRuiz-Postigo J00aThe World Health Organization Skin Neglected Tropical Diseases App: A dynamic capacity building training tool enhanced with artificial intelligence.  uhttps://www.jidonline.org/action/showPdf?pii=S0022-202X%2826%2901185-1  a1 - 363 a<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>
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