Utilization of digital health in the screening, diagnosis, and management of Morbus Hansen
DOI: https://doi.org/10.33024/minh.v9i4.3415
Artificial Intelligence Diagnosis Digital Health Leprosy mHealth Screening
Abstract
Background: Leprosy (Morbus Hansen) remains a significant global health problem, and delayed detection in areas with limited specialist access continues to drive permanent disability. Digital health offers a promising means of strengthening the screening, diagnosis, and management of the disease in such settings.
Purpose: To summarise the most recent evidence on the implementation of digital technologies in leprosy care. Method: A systematic literature search was conducted in the Scopus, PubMed, ScienceDirect, and EBSCO databases up to 21 November 2025, using the keywords leprosy, digital health, mHealth, artificial intelligence, screening, diagnosis, and management. Eligible studies were original research that evaluated digital tools for the screening, diagnosis, or management of leprosy. Study selection followed the PRISMA 2020 guidelines, and the risk of bias was appraised using the Mixed Methods Appraisal Tool (MMAT).
Results: Of 80 records identified, five studies met the inclusion criteria. These covered an artificial intelligence (AI) web-based application for classifying leprosy type, a mobile application supporting primary health care, an e-Leprosy information system with SMS reminders, a machine-learning algorithm based on the Leprosy Suspicion Questionnaire (MaLeSQs), and the AI4Leprosy diagnostic assistant based on skin images. Reported sensitivity ranged from 85.7% to 93.97% and specificity from 69.2% to 90%.
Conclusion: Digital technologies such as mobile applications, health information systems, and AI can enhance leprosy screening and management. However, most studies remain at an early stage and require field validation and the evaluation of their long-term clinical impact.
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