©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.
World J Clin Cases. Mar 6, 2026; 14(7): 118581
Published online Mar 6, 2026. doi: 10.12998/wjcc.v14.i7.118581
Published online Mar 6, 2026. doi: 10.12998/wjcc.v14.i7.118581
Artificial intelligence advancement in addressing cough
Rubi Thapa, Vivek Paudyal, Department of General Practice and Emergency Medicine, Karnali Academy of Health Sciences, Jumla 21200, Nepal
Munish Sharma, Department of Pulmonary and Critical Care Medicine, Baylor Scott and White, Temple, LA 76508, United States
Iqbal Ratnani, Department of Anesthesiology, Houston Methodist, Houston, TX 77030, United States
Salim Surani, Department of Medicine, University of Houston, Houston, TX 77004, United States
Salim Surani, Department of Medicine, Aga Khan University, Nairobi 30270, Nairobi City, Kenya
Author contributions: Sharma M and Surani S conceptualized the study; Thapa R, Paudyal V, Sharma M, and Surani S carried out the literature review and initial drafting of the manuscript; Thapa R prepared the figures and tables; Sharma M, Ratnani I, and Surani S undertook manuscript revision and editing; all authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: None of the authors have any conflict of interest to disclose.
Corresponding author: Salim Surani, MD, FACP, FCCP, Professor, Department of Medicine, University of Houston, 4302 University Drive, Houston, TX 77004, United States.
Received: January 6, 2026
Revised: January 22, 2026
Accepted: February 6, 2026
Published online: March 6, 2026
Processing time: 58 Days and 13.9 Hours
Revised: January 22, 2026
Accepted: February 6, 2026
Published online: March 6, 2026
Processing time: 58 Days and 13.9 Hours
Core Tip
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.