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3/5/2026 9:14:50 AM | Browse: 1 | Download: 1
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Received |
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2026-01-06 07:15 |
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2026-01-06 07:15 |
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2026-01-14 07:57 |
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2026-01-14 07:57 |
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2026-01-22 15:38 |
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Second Decision by Editor |
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2026-02-06 02:48 |
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Final Decision by Editorial Office Director |
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2026-02-06 06:39 |
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Articles in Press |
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2026-02-06 06:39 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-02-10 00:39 |
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Publish the Manuscript Online |
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2026-03-05 07:45 |
| ISSN |
2307-8960 (online) |
| 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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Medicine, General & Internal |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence advancement in addressing cough
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Rubi Thapa, Vivek Paudyal, Munish Sharma, Iqbal Ratnani and Salim Surani |
| Funding Agency and Grant Number |
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| Corresponding Author |
Salim Surani, FACP, FCCP, MD, Professor, Department of Medicine, University of Houston, 4302 University Drive, Houston, TX 77004, United States. srsurani@hotmail.com |
| Key Words |
Acoustics; Artificial intelligence; Cough; Cough sound analysis; Deep learning; Machine learning |
| Core Tip |
Evaluating cough for pulmonary conditions relies substantially on acoustic measurement, yet existing quantification tools remain suboptimal. Integrating artificial intelligence (AI), particularly machine learning and deep learning, offers a promising pathway for both therapeutic and preventive applications in cough medicine. Current implementations remain confined to weak AI’s predefined roles, while advances in artificial general intelligence hold the potential to overcome these adaptability constraints. Moreover, harnessing current potential requires resolving key technical, ethical, and legal issues by integrating explainable AI, multimodal hybrid approaches, and robust accountability measures within human-centered frameworks. |
| Publish Date |
2026-03-05 07:45 |
| Citation |
Thapa R, Paudyal V, Sharma M, Ratnani I, Surani S. Artificial intelligence advancement in addressing cough. World J Clin Cases 2026; 14(7): 118581 |
| URL |
https://www.wjgnet.com/2307-8960/full/v14/i7/118581.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v14.i7.118581 |
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