# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 6 Conclusions
Optical coherence tomography (OCT) provides high-resolution, real-time monitoring with deep tissue penetration but is sensitive to motion artifacts and environmental factors [126]. Other optical methods, such as infrared absorption spectroscopy, allow deep skin penetration but are also sensitive to varied skin conditions [101; 102]. Photoacoustic spectroscopy provides a higher sensitivity to motion artifacts, and environmental conditions limit their application [129]. New techniques like microwave-based glucose sensing have also shown promising results in real-time monitoring on flexible substrates but lack precision [108]. While each technique shows promise, there are still significant challenges to overcome, such as sensitivity to skin conditions, signal interference, and long-term stability. Future advancements should focus on enhancing the sensitivity, specificity, and calibration of these methods, ensuring long-term stability and accuracy in diverse physiological conditions. The convergence of these technologies with the integration of artificial intelligence (AI) and machine learning (ML) presents an exciting future direction for glucose sensing [129]. To improve calibration, AI-driven systems dynamically adjust sensor outputs by learning from historical data and real-time feedback, ensuring more accurate glucose measurements. For example, AI models can correct for sensor drift, environmental factors, and interference, which typically affect glucose sensors, leading to more consistent and reliable results. Machine learning also helps to reduce errors by identifying and mitigating noise from external sources, such as movement or signal interference, which can distort readings [105; 118]. In addition, AI algorithms can integrate data from multiple sources, such as blood glucose readings, wearables, and lifestyle factors, to personalize predictions. For example, AI-powered wrist-worn devices that analyze resonance shifts in the microwave spectrum can predict glucose levels with significant accuracy [113; 118]. These devices utilize machine learning models to calibrate the glucose readings based on real-time data and improve the prediction of glucose trends. Moreover, large-scale trials and datasets can further fine-tune the algorithms, increasing the precision of real-time predictions, which could ultimately reduce the need for invasive procedures and enhance overall diabetes management. The integration of AI and machine learning into glucose-monitoring systems enhances their ability to forecast glucose levels, calibrate sensors for greater accuracy, and reduce errors through continuous learning and adaptation. This represents a significant step forward in improving diabetes management and patient outcomes. K.D. : Conceptualization, data curation and writing original draft. T.S.S. : Technical suggestions for the manuscript. F.G.T. : Review of draft, Supervision. All authors have read and agreed to the published version of the manuscript. Authors would like to thank the Ministry of Science and Technology (MOST), Taiwan, MOST110-2221-E-007-017-MY3, MOST111-2923-E-007-006-MY3, National Health Research Institute, Taiwan, NHRI-EX111-11126 BI, and Ministry of Education (MOE), Taiwan, Frontier Research Centre on Fundamental and Applied Sciences of Matters (111 QR00115, MOST 111-2634-F-007-007) for providing financial support for this research project. No new data were created or analyzed in this study. The authors acknowledge \"Indian Institute of Technology, Madras\" and \"National Tsing Hua University\" Partnership Collaboration. The authors declare no conflict of interest.