Advances in diagnosis of lung fibrosis: focus on present and future approaches.

Background: Lung fibrosis encompasses a group of interstitial lung diseases (ILDs) characterized by progressive scarring of lung tissue, often leading to respiratory failure and high mortality. Contemporary diagnostic frameworks have evolved from diagnosis-centered to pattern-based approaches, incorporating the concept of progressive pulmonary fibrosis (PPF) as a unifying clinical entity. Accurate and timely diagnosis is critical for guiding appropriate management but remains challenging due to non-specific symptoms, overlapping radiological patterns, and limitations of existing diagnostic tools. We aimed to summarize the status, limitations, and emerging approaches in the diagnosis of lung fibrosis, with an emphasis on imaging modalities, histopathological techniques, molecular diagnostics, artificial intelligence (AI) applications, and to propose an updated diagnostic algorithm. Evidence and methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar to identify relevant literature published between January 2015 and February 2026. Priority was given to international clinical guidelines, consensus statements, systematic reviews, meta-analyses, and clinically relevant original studies addressing diagnostic approaches to lung fibrosis and interstitial lung diseases. The retrieved evidence was synthesized thematically, focusing on imaging modalities, histopathological techniques, molecular diagnostics, biomarkers, artificial intelligence, and multidisciplinary diagnostic frameworks. Results: High-resolution computed tomography (HRCT) remains the gold standard for non-invasive diagnosis, with pattern classification guided by the 2022 ATS/ERS/JRS/ALAT guidelines. MRI, lung ultrasound, and functional imaging offer valuable adjuncts. Surgical lung biopsy provides histopathological confirmation but carries a risk that varies depending on patient selection; transbronchial lung cryobiopsy (TBLC) has emerged as a less invasive alternative with diagnostic yields exceeding 80% in multidisciplinary settings. Emerging techniques, including gene expression profiling, telomere length assessment, circulating biomarkers, endobronchial optical coherence tomography, and AI-enhanced imaging, show promise for improving early and accurate diagnosis but remain adjuncts to multidisciplinary discussion (MDD) rather than replacements. Home-based monitoring technologies and molecular imaging have expanded capabilities for longitudinal disease monitoring. Despite these advancements, persistent challenges include diagnostic variability, limited access to advanced modalities, and the absence of standardized diagnostic algorithms. Conclusion: In summary, advances in imaging, molecular diagnostics, and artificial intelligence are improving the early and accurate diagnosis of lung fibrosis. Importantly, these tools are complementary to, not substitutes for, multidisciplinary care. Their integration into MDD-centered frameworks is essential to improve patient outcomes.