Non-Invasive Glucose Monitoring Technologies: A ComprehensiveReview of Wearables, AI, and Emerging Technologies

Non-Invasive Glucose Monitoring Technologies: A ComprehensiveReview of Wearables, AI, and Emerging Technologies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Non-Invasive Glucose Monitoring Technologies: A ComprehensiveReview of Wearables, AI, and Emerging Technologies MERIEM ZERKOUK, MILOUD MIHOUBI, Belakcem CHIKHAOUI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7879181/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Diabetes mellitus, a global health challenge affecting millions, necessitates advanced solu-tions for continuous glucose monitoring (CGM) to improve disease management and mitigatecomplications. Conventional methods such as glycated hemoglobin (HbA1c) and fingerstickblood glucose tests are limited by invasiveness, intermittent data, and patient discomfort. Thiscomprehensive review explores cutting edge non invasive glucose monitoring technologies,emphasizing their potential to transform diabetes care through wearable sensors, artificialintelligence (AI), and predictive analytics. We systematically evaluate emerging approaches thatleverage alternative biofluids (e.g., sweat, saliva, tears) and optical sensing modalities, includingnear infrared (NIR) spectroscopy, Raman spectroscopy, and smart contact lens biosensors. Theseinnovations address key challenges in patient safety, usability, and real time data accuracy.Furthermore, we highlight the role of machine learning (ML) and deep learning (DL) inenhancing glucose prediction models, enabling adaptive monitoring and personalized insights.The integration of Large Language Models (LLMs) into digital health platforms is also discussed,showcasing their potential to support clinical decision making and patient empowerment.By bridging engineering advancements with clinical applications, these technologies promiseseamless integration into wearable health devices and e-health ecosystems, fostering proactive,data-driven diabetes management. This review underscores the transformative impact of noninvasive monitoring on health technology assessment, disease management, and patient centeredcare, while identifying future directions for research and implementation. Non-invasive glucose monitoring Artificial intelligence in healthcare Diabetes management Optical biosensors Wearable health sensors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 16 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7879181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":547559048,"identity":"d84b8b73-34bc-4fdf-b8fb-194e849bc4f7","order_by":0,"name":"MERIEM 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