02376nas a2200349   4500000000100000008004100001260002100042653003900063653002300102653002000125653001700145653002000162653001800182653002100200653001900221653002800240653001200268100001300280700001200293700001700305700001700322700001400339700001300353700001600366700001300382245011700395856006600512300001000578490000700588520141700595022001402012       2026                            d  c10/2026bMDPI AG10aNeglected tropical diseases (NTDs)10aPrecision medicine10aEarly diagnosis10aTelemedicine10aComputer vision10aDeep Learning10amachine learning10aDigital health10aArtificial Intelligence10aleprosy1 aRivera G1 aRubio R1 aRodríguez L1 aRodríguez J1 aSalinas A1 aOrozco D1 aGonzález F1 aMoreno M00aArtificial Intelligence and Digital Health in the Diagnosis of Leprosy: Current Advances and Future Perspectives  uhttps://www.mdpi.com/2813-9054/71/4/44/pdf?version=1792655862  a1 - 90 v713 a<jats:p>Leprosy remains a significant public health challenge in endemic regions, where delayed diagnosis leads to irreversible nerve damage, disability, and ongoing transmission. To address the need for innovative diagnostic strategies, artificial intelligence (AI) and digital health technologies have emerged as promising tools. This review explores the current applications of machine learning, deep learning, computer vision, and mobile health platforms in leprosy diagnosis and management. AI-based image analysis demonstrates strong potential for identifying characteristic skin lesions and differentiating leprosy from other dermatological conditions. Additionally, predictive algorithms and smartphone-based telemedicine expand access to specialized care in underserved settings while facilitating contact tracing, surveillance, and treatment monitoring. Despite these encouraging advances, significant challenges remain, including limited high-quality annotated datasets, algorithmic bias, a lack of external validation, and data privacy concerns. Ultimately, AI-driven digital health solutions should complement rather than replace clinical expertise. Prioritizing multicenter collaborations, explainable AI models, and secure data integration will be essential to strengthen global leprosy control programs, improve patient outcomes, and reduce the burden of this neglected tropical disease.</jats:p>  a2813-9054