Understanding Continuous Glucose Monitoring: A Guide to CGM Devices, and how developers can build solutions on such data

The integration of Artificial Intelligence (AI) in diabetes management brings along various ethical challenges that need to be addressed.

LLMs hold immense potential for improving patient care, enhancing communication, and providing personalised support.

This article is the first in a series on the future of diabetes management.

This article provides a comprehensive overview of diabetes, including its types, symptoms, and current management strategies.

This final article in the series provides a self-assessment of our project on AI-driven diabetes management.

This article explores the future directions of diabetes management technology.

By analysing temporal data, healthcare professionals can identify trends, patterns, and potential anomalies, leading to more effective and personalised care.

Predictive modeling enables the prediction of blood glucose levels based on dietary intake and other factors.

Not all carbohydrates have the same effect on blood glucose levels.

Dietary recall questionnaires provide a wealth of information that can be analysed to uncover patterns and correlations.

This article explores various machine learning methods used in predicting and managing diabetes.

The management of Type 1 diabetes has evolved significantly with advancements in technology, particularly in the development of artificial pancreas systems.