AI-Powered Precision for Diabetes Diagnosis and Data Analysis

Presenter

JONATHAN A. CHIN

Campus

Springfield Technical Community College

Sponsor

Reena Randhir, Department of Biology, Springfield Technical Community College

Schedule

Session 5, 3:30 PM - 4:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board C35, Poster Showcase Room (163), Row 4 (C31-C40) [Poster Location Map]

Abstract

Research data shows that as of 2023, half a billion people in the world have diabetes, and expected to double by 2050. This will impose a huge toll on human health and the healthcare systems worldwide. Hence there is a growing need to reduce the barrier in diagnosis and management of this disease. Faster diagnosis can avoid long-term health consequences.  Although current technology has helped to lower costs, more assistance is needed in reducing diabetic treatment, and economic inequity through promising technology in Artificial Intelligence (AI) and deep learning programs such as integration of cloud artificial intelligence-based computing into wearable devices such as continuous glucose monitoring to predict blood glucose levels.  The objective of my research is to explain the potential of AII- powered precision techniques for enhancing the accuracy of diabetes diagnosis and using data analysis for optimal diabetes management. My findings are that this is an important tool used to identify patients with diabetic retinopathy, analyze data sets, and integrate cloud-based computing into current technology on a small scale. The drawback is that because artificial intelligence is in its infancy, and has only been recently implemented in some hospitals, the data sets to measure its effectiveness are small and currently cannot accurately diagnose diabetic retinopathy more accurately than humans. AI cloud-based computing into wearable devices was able help predict patient’s blood glucose levels although more data is needed. 

Keywords

Diabetes Type 1, Diabetes Type 2, Artificial Intelligence, Chronic Disease, Insulin Technology

Research Area

Artificial Intelligence

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