Non-Invasive Blood Glucose Monitoring Using Machine Learning and Optical Approaches: A Systematic Literature Review and a Proposed Framework for Reproducible and Clinically Reliable Research

Abstract

Diabetes Mellitus is the leading healthcare challenge. For effective management of this disease, patients need to check their blood glucose levels regularly; however, the standard method of taking a drop of blood and placing it on a test strip does not allow for continuous monitoring and is dreaded by most people, who find it painful and uncomfortable. Researchers worldwide are trying to develop non-invasive blood glucose monitoring (NIBGM) devices; most of them have reported promising accuracy using a combination of optical techniques and machine learning, but the CE/FDA marked solution still cannot reach the market. The main objective of this systematic review is to critically analyse recent quantitative research works in optical NIBGM, highlight their strengths, and identify the main factors that hinder regulatory approvals and the commercialisation of these devices. Web of Science, IEEE Xplore, PubMed, ScienceDirect, SPIE Digital Library, and Optica Publishing Group databases were searched between 1st September 2015 and 1st August 2026 using specific keywords. From the initial 1143 records, 245 studies were considered for full-text quality assessment, and 208 were excluded based on a post hoc quality score threshold. The sensing modality, wavelength, sample size, reference device, glucose range, validation strategy, accuracy metrics, and use of demographic variables as model inputs for the 37 included studies are reported. By applying an author-defined three-tier rubric, the risk of shortcut learning in each study was highlighted. Based on these findings, a common framework of fifteen concrete, evidence-grounded recommendations for future NIBGM studies was proposed.