TY - JOUR KW - Neglected tropical diseases (NTDs) KW - Precision medicine KW - Early diagnosis KW - Telemedicine KW - Computer vision KW - Deep Learning KW - machine learning KW - Digital health KW - Artificial Intelligence KW - leprosy AU - Rivera G AU - Rubio R AU - Rodríguez L AU - Rodríguez J AU - Salinas A AU - Orozco D AU - González F AU - Moreno M AB - 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. BT - Acta Microbiologica Hellenica DA - 10/2026 DO - 10.3390/amh71040044 IS - 4 LA - ENG M3 - Article N2 - 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. PB - MDPI AG PY - 2026 SP - 1 EP - 9 T2 - Acta Microbiologica Hellenica TI - Artificial Intelligence and Digital Health in the Diagnosis of Leprosy: Current Advances and Future Perspectives UR - https://www.mdpi.com/2813-9054/71/4/44/pdf?version=1792655862 VL - 71 SN - 2813-9054 ER -