Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999–2025]
Luna Zhao, Lin Chen, Fangyi Zhao, Jinyang Li, Hanwen Zhang, Yue Zhou, Jingkun Li, Rongchen Jin, Shengnan Zhang, Ye Liu, Yifei Yao, Chao Wu, Dong Liu · 2026
Read Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999–2025] by Luna Zhao, Lin Chen, Fangyi Zhao, Jinyang Li, Hanwen Zhang, Yue Zhou, Jingkun Li, Rongchen Jin, Shengnan Zhang, Ye Liu, Yifei Yao, Chao Wu, Dong Liu (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13559443.txt.
Description
Zhao and colleagues map global research on diagnosing pneumoconiosis, the lung disease caused by years of inhaling industrial dust, against where the disease burden actually falls, in 2026 bibliometric research. Drawing on the Web of Science database, they analyzed 540 articles published from 1999 to 2025, and compared output and themes with Global Burden of Disease 2021 figures. Traditional diagnosis relies on work history and chest images, which readers judge unevenly and which miss early disease.
Publication output followed an S-shaped curve, accelerating after 2015 as deep learning entered the field. The United States and China dominated production, while Southeast Asia and sub-Saharan Africa, regions carrying heavy burdens, stayed at the field's edge. Research themes moved through three phases: conventional radiology, quantitative CT scanning, and AI-enabled systems. Collaboration between respiratory medicine, imaging, and computer science intensified over time. Zhao argues new diagnostics are not reaching the regions that need it most.
Catalog record
| CREATOR | Luna Zhao, Lin Chen, Fangyi Zhao, Jinyang Li, Hanwen Zhang, Yue Zhou, Jingkun Li, Rongchen Jin, Shengnan Zhang, Ye Liu, Yifei Yao, Chao Wu, Dong Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |