Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Apr 28, 2022; 3(2): 66-79
Published online Apr 28, 2022. doi: 10.35712/aig.v3.i2.66
Artificial intelligence in critically ill diabetic patients: current status and future prospects
Deven Juneja, Anish Gupta, Omender Singh
Deven Juneja, Anish Gupta, Omender Singh, Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
Author contributions: Juneja D and Gupta A performed the majority of the writing, prepared the tables and performed data accusation; Singh O provided the input in writing the paper and reviewed the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Deven Juneja, FCCP, MBBS, Director, Institute of Critical Care Medicine, Max Super Speciality Hospital, 1, Press Enclave Road, Saket, New Delhi 110092, India.
Received: February 16, 2022
Peer-review started: February 16, 2022
First decision: April 17, 2022
Revised: April 21, 2022
Accepted: April 28, 2022
Article in press: April 28, 2022
Published online: April 28, 2022

Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.

Keywords: Artificial intelligence, Blood glucose, Critical care, Diabetes mellitus, Intensive care unit, Machine learning

Core Tip: Increasing number of applications and devices based on artificial intelligence are being tested and approved for medical care. These devices have the potential to change the way we presently manage chronic diseases like diabetes. Moreover, their application in data rich and dynamic intensive care unit environment may have great implications in detecting hypo or hyperglycemia and reducing glycemic variability, while improving safety and accuracy and reducing nursing workload. Devices like artificial pancreas and continuous glucose monitoring regulated insulin infusion systems have shown promise as comprehensive glucose control solutions and may change the future of care for critically ill diabetic patients.