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<p>Diabetes Mellitus affects 8.8% worldwide and is projected to reach 10.9% by 2045, increasing the need for advanced diagnosis and treatment. Technology-driven solutions are recommended for better management and timely interventions. Recently, the Internet of Things (IoT) has penetrated the medical sector, presenting the Internet of Medical Things (IoMT) as a new paradigm. Moreover, it has been integrated with technologies such as blockchain to enhance security levels and Artificial Intelligence (AI) to improve decision-making. This paper presents a concentrated study of the most relevant work that links IoMT with industry to help researchers, developers, and medical service providers accelerate improvements in medical services. Diabetes is selected as a case study. This paper reviews the most recent work on remote patient monitoring (RPM) and continuous glucose monitoring (CGM) to improve decision support systems, increasing the precision of prediction of critical events related to diabetes in daily life. In addition, it highlights the role of blockchain-based platforms in improving security, integrity, immutability, and decentralized healthcare solutions. Given the increasing focus on industrial diabetes management solutions, a comparative analysis of existing IoMT-based diabetes therapy systems is presented, shedding light on promising technologies that could fulfill essential requirements to enable smarter and more secure healthcare ecosystems. Given the urgency of this global challenge, diabetes management has become one of the most attractive research directions, especially when combined with emerging technologies such as IoMT, blockchain, and AI, which open new frontiers for smarter and more secure healthcare. The review was conducted using major and trusted digital libraries, including IEEE Xplore, SpringerLink, ScienceDirect, PubMed, and SAGE Journals .Coverage was cross-verified using Scopus and Web of Science to ensure completeness. In total, 1,120 papers were retrieved, of which 127 met the inclusion criteria for detailed analysis.</p>

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Diabetes challenges and promising solutions based on the internet of medical things, blockchain, and artificial intelligence: A systematic literature review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Mina Younan,
  • Amir Mostafa,
  • Mina Ezzat Attya,
  • Essam H. Houssein

摘要

Diabetes Mellitus affects 8.8% worldwide and is projected to reach 10.9% by 2045, increasing the need for advanced diagnosis and treatment. Technology-driven solutions are recommended for better management and timely interventions. Recently, the Internet of Things (IoT) has penetrated the medical sector, presenting the Internet of Medical Things (IoMT) as a new paradigm. Moreover, it has been integrated with technologies such as blockchain to enhance security levels and Artificial Intelligence (AI) to improve decision-making. This paper presents a concentrated study of the most relevant work that links IoMT with industry to help researchers, developers, and medical service providers accelerate improvements in medical services. Diabetes is selected as a case study. This paper reviews the most recent work on remote patient monitoring (RPM) and continuous glucose monitoring (CGM) to improve decision support systems, increasing the precision of prediction of critical events related to diabetes in daily life. In addition, it highlights the role of blockchain-based platforms in improving security, integrity, immutability, and decentralized healthcare solutions. Given the increasing focus on industrial diabetes management solutions, a comparative analysis of existing IoMT-based diabetes therapy systems is presented, shedding light on promising technologies that could fulfill essential requirements to enable smarter and more secure healthcare ecosystems. Given the urgency of this global challenge, diabetes management has become one of the most attractive research directions, especially when combined with emerging technologies such as IoMT, blockchain, and AI, which open new frontiers for smarter and more secure healthcare. The review was conducted using major and trusted digital libraries, including IEEE Xplore, SpringerLink, ScienceDirect, PubMed, and SAGE Journals .Coverage was cross-verified using Scopus and Web of Science to ensure completeness. In total, 1,120 papers were retrieved, of which 127 met the inclusion criteria for detailed analysis.