The clinical course of pneumoconiosis: An examination of diagnosis and follow-up in the light of current literature

This study aims to systematically review the current literature on the clinical course, diagnostic approaches, and follow-up protocols of pneumoconiosis. The disease was examined in terms of clinical, radiological, biomarker-based, and technological perspectives to provide a comprehensive overview of diagnostic and monitoring strategies. Methods: Peer-reviewed research articles, systematic reviews, meta-analyses, and international guidelines published between 2005 and 2025 were screened in PubMed, Scopus, Web of Science, and Google Scholar databases. The keywords "pneumoconiosis," "clinical course," "diagnosis," "follow-up," and "occupational lung disease" were used with Boolean operators (AND, OR). Studies were selected according to PRISMA criteria and evaluated based on diagnostic standards, monitoring methods, and methodological quality. Results: The review demonstrated that high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs) are the most reliable tools for early detection of pneumoconiosis. Biomarkers such as KL-6, SP-D, and TGF-β serve as indicators of fibrotic activity and clinical progression. Artificial intelligence (AI)-based HRCT analysis and digital monitoring systems have enhanced diagnostic accuracy and follow-up sensitivity. Furthermore, antifibrotic agents (pirfenidone, nintedanib) and tele-monitoring technologies emerged as promising advances in clinical management. Conclusion: Effective management of pneumoconiosis requires early diagnosis, systematic follow-up, multidisciplinary collaboration, and integration of digital technologies. AI-assisted imaging and biomarker-guided monitoring approaches represent the future of personalized clinical management in occupational lung diseases. Keywords: Pneumoconiosis, Clinical course, Diagnosis, Follow-up, Artificial intelligence, Biomarkers, Digital health