Optical Coherence Tomography and Optical Polarimetry for Non-Invasive Glucose Monitoring: Principles, Recent Advances, Research Gaps, and Future Directions

Diabetes mellitus is a major global public health challenge, with prevalence rising due to aging populations, urbanization, sedentary lifestyles, and dietary changes. Effective glucose monitoring is essential for diagnosis, treatment, and long-term disease management, driving significant research into improved sensing technologies. Conventional invasive and minimally invasive glucose monitoring methods often cause discomfort and reduce patient compliance, motivating the development of non-invasive alternatives. Recent advances in photonics, biomedical engineering, nanotechnology, wearable devices, and artificial intelligence have accelerated the emergence of innovative glucose sensing approaches capable of improving comfort, safety, and monitoring frequency. This review presents a comprehensive overview of recent non-invasive glucose monitoring technologies reported in the literature, including optical, electromagnetic, nanotechnology-based, and physiological sensing methods evaluated through human studies, biological samples, or tissue-equivalent models. The underlying sensing principles, measurement sites, performance characteristics, and practical implementation challenges of these technologies are discussed. Particular attention is given to the integration of machine learning algorithms, which have demonstrated significant potential for enhancing glucose prediction accuracy and supporting real-time monitoring applications. The review also critically examines the advantages, limitations, clinical feasibility, and commercialization prospects of existing technologies, highlighting the key barriers that continue to impede widespread adoption. By consolidating recent developments across multiple scientific and engineering disciplines, this work provides researchers, clinicians, and technology developers with a concise assessment of the current state of non-invasive glucose sensing and identifies future research directions necessary for advancing reliable, accurate, and user-friendly next-generation diabetes management systems.