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The Background: Diabetes mellitus (DM) is a chronic disease that demands extensive long-term management. Advances in both Mobile health (mHealth) technologies Artificial intelligence (AI) has the power to revolutionize the DM care, and their combination can serve in many fields such as predictive analytics, personalized treatment plans, and real-time monitoring, aiding early diagnosis, complication prevention, and patient empowerment to manage their health effectively, reducing the global DM burden. Objective: To summarize existing findings on mHealth using AI for diabetes management. Methods: Systematic review of original research between 01/01/2019 and 10/06/2024. A search covered four databases on (Scopus), (Web of Science), (PubMed Mesh), and (PubMed Advanced). Results: From 303 search results, 61 articles were selected after using the eligibility criteria.. 59% used a smartphone as principal mobile device. 37% used machine learning models. More than 28.9% used deep learning models. And more than 8.1% used natural language understanding and automated chatbots. Conclusion: This review underscores AI and mobile health’s promising intersection in diabetes management, highlighting potential benefits and the need for more robust clinical trials.

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Harnessing Artificial Intelligence in Mobile Health for Diabetes Management: A Systematic Review

  • 会议论文

  • 发表日期：

    2025年

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  • Hind Bourkhime,
  • Noura Qarmiche,
  • Mohammed Omari,
  • Nouhaila Charef,
  • Somaia Elghazi,
  • Nabil Tachfouti,
  • Samira El Fakir,
  • Mohamed Berraho,
  • Nada Otmani

摘要

The Background: Diabetes mellitus (DM) is a chronic disease that demands extensive long-term management. Advances in both Mobile health (mHealth) technologies Artificial intelligence (AI) has the power to revolutionize the DM care, and their combination can serve in many fields such as predictive analytics, personalized treatment plans, and real-time monitoring, aiding early diagnosis, complication prevention, and patient empowerment to manage their health effectively, reducing the global DM burden. Objective: To summarize existing findings on mHealth using AI for diabetes management. Methods: Systematic review of original research between 01/01/2019 and 10/06/2024. A search covered four databases on (Scopus), (Web of Science), (PubMed Mesh), and (PubMed Advanced). Results: From 303 search results, 61 articles were selected after using the eligibility criteria.. 59% used a smartphone as principal mobile device. 37% used machine learning models. More than 28.9% used deep learning models. And more than 8.1% used natural language understanding and automated chatbots. Conclusion: This review underscores AI and mobile health’s promising intersection in diabetes management, highlighting potential benefits and the need for more robust clinical trials.