Global research landscape versus disease burden in pneumoconiosis diagnosis: a dual-source bibliometric analysis [1999-2025].

BACKGROUND: Pneumoconiosis, a preventable occupational lung disease caused by prolonged dust inhalation, remains a major global health burden. Traditional diagnosis relying on occupational history and chest imaging is limited by inter-observer variability and poor early-stage sensitivity. Although artificial intelligence (AI) offers promising solutions, the global research landscape and its alignment with epidemiological needs have not been systematically evaluated. To bridge this gap, we conducted a dual-source analysis to quantify the global research output, thematic evolution, and its alignment with regional pneumoconiosis burdens from 1999 to 2025. METHODS: Here, we retrieved publications on pneumoconiosis diagnosis from the Web of Science database [1999-2025], including 540 articles based on PRISMA 2020 guidelines, and performed bibliometric analyses using CiteSpace, VOSviewer, and R program. Global Burden of Disease (GBD) 2021 data served as an external benchmark for disease burden. RESULTS: Publication output exhibited an S-shaped growth, accelerating after 2015 with deep learning integration. The United States and China dominated the research field, while high-burden regions (Southeast Asia and sub-Saharan Africa) remained peripheral. The field evolved through three phases: conventional radiology, quantitative computed tomography (CT), and AI-enabled systems. Interdisciplinary collaborations among the fields of respiratory medicine, imaging, and computer science intensified. Keyword bursts highlighted deep learning and emerging exposures (e.g., artificial stone silicosis) as current frontiers of research. A persistent geographic mismatch was evident: high-burden countries produced far fewer publications than low-burden counterparts. CONCLUSIONS: Pneumoconiosis diagnostics has shifted from manual assessment to AI-powered quantification. Future priorities should include explainable AI (XAI) validation, multimodal data integration, updated criteria for novel dust exposures, and equitable global collaboration to bridge research-burden disparities.