An Analysis of Explainable Artificial Intelligence Implementation in Mobile Health-Based Disease Diagnosis Systems in Indonesia
Keywords:
Explainable Artificial Intelligence, Mobile Health, Artificial Intelligence, Disease Diagnosis Systems, User Trust and InterpretabilityAbstract
This study investigates the implementation of Explainable Artificial Intelligence in mobile health (mHealth)-based disease diagnosis systems in Indonesia, focusing on improving transparency, user understanding, and trust. The growing integration of Artificial Intelligence in healthcare has enhanced diagnostic efficiency and accessibility; however, many systems still function as “black-box” models, limiting interpretability and reducing user confidence. This study addresses the gap between high diagnostic accuracy and low explainability in mHealth applications. A mixed-method approach was used, combining quantitative and qualitative data. The research analyzed selected mHealth applications to assess the availability and effectiveness of explainability features. In addition, user data were collected through surveys and semi-structured interviews involving patients and healthcare professionals. Quantitative data were analyzed statistically to examine relationships between explainability, user understanding, and trust, while qualitative data were explored through thematic analysis to capture user experiences and perceptions. The findings reveal that XAI significantly enhances users’ understanding of AI-generated diagnoses, particularly when explanations are simple, visual, and context-specific. Improved understanding was found to positively influence user trust and acceptance of mHealth systems. However, the study also identifies a trade-off between interpretability and model performance, along with challenges related to digital literacy, infrastructure, and usability. Furthermore, the effectiveness of explainability depends on user characteristics and the design of explanation mechanisms. Overall, this research provides empirical insights into the practical implementation of XAI in mHealth systems and offers recommendations for developers, policymakers, and healthcare institutions to design more transparent, user-centered, and trustworthy AI-driven healthcare solutions.
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Copyright (c) 2026 Galih Rakasiwi, Khanza Khanza, Aulia Izzatunnisa
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