Artificial Intelligence and Digital Health in the Diagnosis of Leprosy: Current Advances and Future Perspectives

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.