Nag DS, Swain A, Sahu S, Chatterjee A, Swain BP. Relevance of sleep for wellness: New trends in using artificial intelligence and machine learning. World J Clin Cases 2024; 12(7): 1196-1199 [PMID: 38524514 DOI: 10.12998/wjcc.v12.i7.1196]
Corresponding Author of This Article
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
Research Domain of This Article
Multidisciplinary Sciences
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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.
