Interstitial Lung Disease in 2025: Updated Classification, Precision Diagnostics, and Expanding Therapeutic Frontiers
Bibliographic record
Abstract
Interstitial lung disease (ILD) is an umbrella term for over 200 heterogeneous disorders that primarily affect the pulmonary interstitium and/or small airways and alveoli. These disorders are often characterized by varying degrees of inflammation and fibrosis that lead to impairment in lung function and consequent respiratory symptoms. Although idiopathic pulmonary fibrosis (IPF) remains the classic example, there exists a remarkable prevalence of cases in the context of connective tissue disease (CTD) or as a consequence of antigen exposure in the setting of hypersensitivity pneumonitis (HP). Over the past decade, the integration of high-resolution computed tomography (HRCT), extended serologic panels, increased knowledge of the natural history of disease, and the high risk of fibrotic progression across the diagnostic spectrum, has refined diagnostic certainty and allowed earlier therapeutic intervention. Contemporary guidelines from the American Thoracic Society (ATS), European Respiratory Society (ERS), and European Alliance of Associations for Rheumatology (EULAR) now emphasize pattern-based phenotyping rather than rigid disease taxonomies. Clinicians are encouraged to interpret ILD through intersecting dimensions—radiologic morphology, immune serology, and cellular aging biology—each contributing to disease trajectory and treatment response. For Canadian respirologists, these advances coincide with a rapidly evolving therapeutic landscape (Table 1). Antifibrotic agents, selective immunomodulators, and emerging inhaled prostacyclin-based therapies are transforming the future therapeutic landscape. Yet, access to evidence-based treatments, especially for those with systemic sclerosis (SSc) and idiopathic inflammatory myopathies (IIM) related lung disease, remains inconsistent across provinces, underscoring the need for sustained advocacy. This review summarizes the 2025 classification framework, outlines novel diagnostic approaches, and appraises recent and emerging clinical trial data with practical guidance for clinical practice (Table 2).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".