Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1]
Corresponding Author of This Article
Pahnwat Tonya Taweesedt, MD, Academic Fellow, Department of Pulmonary Medicine, Corpus Christi Medical Center, 3315 S Alameda St., Corpus Christi, TX 78411, United States. pahnwatt@gmail.com
Research Domain of This Article
Respiratory System
Article-Type of This Article
Minireviews
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/
Saiara Choudhury, Asad Chohan, Rahul Dadhwal, Abhay P Vakil, Rene Franco, Pahnwat Tonya Taweesedt, Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
Author contributions: Choudhury S performed the majority of the writing; Chohan AA provided the input in writing the paper; Dadhwal R provided the input in writing the paper; Abhay V provided the input in writing the paper; Franco R provided the input in writing the paper; Taweesedt PT designed the outline and coordinated the writing of the paper.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to report.
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: Pahnwat Tonya Taweesedt, MD, Academic Fellow, Department of Pulmonary Medicine, Corpus Christi Medical Center, 3315 S Alameda St., Corpus Christi, TX 78411, United States. pahnwatt@gmail.com
Received: December 13, 2021 Peer-review started: December 13, 2021 First decision: January 26, 2022 Revised: February 14, 2022 Accepted: February 17, 2022 Article in press: February 17, 2022 Published online: February 28, 2022 Processing time: 77 Days and 1.9 Hours
Abstract
Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.
Core Tip: Artificial Intelligence (AI) has the potential to have a tremendous influence when dealing with pulmonary diseases. This review provides a glimpse of AI application in pulmonary medicine and explains how AI uses imaging data to facilitate precision medicine in our data-driven era.
