Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Mar 6, 2024; 12(7): 1196-1199
Published online Mar 6, 2024. doi: 10.12998/wjcc.v12.i7.1196
Relevance of sleep for wellness: New trends in using artificial intelligence and machine learning
Deb Sanjay Nag, Amlan Swain, Seelora Sahu, Abhishek Chatterjee, Bhanu Pratap Swain
Deb Sanjay Nag, Amlan Swain, Seelora Sahu, Abhishek Chatterjee, Bhanu Pratap Swain, Department of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India
Author contributions: Nag DS, Swain A, Sahu S, Chatterjee A, Swain BP contributed to this paper; Nag DS and Swain A designed the overall concept and outline of the manuscript; Swain A, Sahu S, Chatterjee A contributed to the discussion and design of the manuscript; Nag DS, Swain A, Sahu S, Chatterjee A and Swain BP contributed to the writing, and editing the manuscript and review of literature.
Conflict-of-interest statement: The authors declare 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Deb Sanjay Nag, MBBS, MD, Doctor, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, Jharkhand, India. ds.nag@tatasteel.com
Received: December 26, 2023
Peer-review started: December 26, 2023
First decision: January 15, 2024
Revised: January 16, 2024
Accepted: February 5, 2024
Article in press: February 5, 2024
Published online: March 6, 2024
Processing time: 65 Days and 20.7 Hours
Core Tip

Core Tip: Quality sleep is one of the major determinants of wellness. Insomnia and other sleep disorders are widespread in the society. Increasingly, technology is being used to diagnose sleep disorders through wearable devices and consumer technologies. This has allowed sleep disorders to be diagnosed at home rather than at polysomnography labs. With the advent of artificial intelligence, including machine and deep learning, sleep disorder diagnosis has become highly dynamic based on multiple inputs and complex algorithms analyzing huge quantum of metadata. Similarly, therapy is also becoming highly patient-specific due to available digital tools. However, the ever-expanding knowledge needs further validation to establish patient-centric care.