Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care

Abstract

This review explores the integration of artificial intelligence (AI) in mobile health applications for diabetes care. It focuses on key AI methodologies - machine learning, deep learning, and natural language processing - and their roles in glucose monitoring, personalized self-management, risk prediction, and clinical decision support. Drawing on recent literature (2018-2024), the study outlines the benefits of AI in improving accuracy, engagement, and precision in diabetes treatment. Challenges such as data privacy, algorithmic bias, and regulatory barriers are also examined. A new section discusses when AI technologies may become burdensome, especially in low-resource settings or for users with limited digital literacy. The review concludes with directions for enhancing model explainability and integrating AI with wearable and Internet of Things devices, emphasizing the need for ethical and equitable implementation in future diabetes management strategies.

Keywords: Artificial intelligence; Mobile health; Diabetes management; Predictive analytics; Clinical decision support; Personalized self-management

Core Tip: This review highlights the transformative role of artificial intelligence in mobile health applications for diabetes care. It synthesizes recent advances in machine learning, deep learning, and natural language processing, examining their use in glucose monitoring, personalized interventions, and clinical decision support. The review also discusses ethical challenges, data privacy, and situations where artificial intelligence may become burdensome. By bridging technology and practice, this study offers insights into building more equitable, efficient, and patient-centered diabetes management systems.