JOURNEY FROM SYMPTOM RECOGNITION TO SCREENING APPROACHES: A LITERATURE REVIEW ON DIABETES

Mustafa H. Ghazi*, Riyadh Abdulkareem and Ayat Adnan Abbas

This literature review aims to assess the diagnosis of diabetes, focusing on the significance of early identification and a comprehensive examination of the challenges associated with symptom-based diagnosis. The review explores multiple screening methods: the glycated hemoglobin (HbA1c) test, random plasma glucose test, oral glucose tolerance test, and fasting plasma glucose (FPG) test. Furthermore, it analyzes the potential enhancements in screening accuracy that can be achieved through advancements in diagnostic technologies, including continuous glucose monitoring (CGM), point-of-care testing (POCT), artificial intelligence (AI), machine learning (ML), and wearable devices. The analysis also highlights the obstacles encountered in diabetes diagnosis, such as the unpredictability of symptoms and limited accessibility to healthcare providers. As a result, this study puts forth prospective remedies, encompassing the reinforcement of educational and awareness campaigns, enhancement of healthcare availability, formulation of ethnicity-specific algorithms, advocacy for data exchange and standardization, integration of machine learning and artificial intelligence (ML/AI) into clinical settings, and adoption of longitudinal monitoring approaches. Ultimately, the manuscript concludes by underscoring the pivotal role played by early detection in proficiently managing diabetes and averting complications while simultaneously accentuating the necessity for additional research to validate these technologies and augment diagnostic capacities.