Artificial intelligence-based chest CT quantification technology is beginning to move beyond simple diagnostic assistance into the broader areas of long-term prognosis prediction and drug development for chronic lung diseases.
Korean medical AI company Coreline Soft announced that its chest imaging AI platform AVIEW demonstrated clinical value in predicting disease progression and survival outcomes in patients with interstitial lung disease (ILD), one of the most challenging respiratory disorders associated with progressive pulmonary fibrosis.
According to researchers from the Departments of Radiology and Pulmonary Medicine at Asan Medical Center in Seoul, the study confirmed that changes in the AI-derived Fibrosis Score (FS), generated using AVIEW, could serve as a meaningful prognostic indicator for ILD progression and patient survival. The findings were recently published in the May 2026 issue of the global respiratory medicine journal CHEST.
The Fibrosis Score is calculated through AI analysis of chest CT scans by quantifying key imaging patterns associated with pulmonary fibrosis, including reticulation and honeycombing. Unlike conventional pulmonary function tests (PFTs), which can be influenced by patient cooperation and variability in interpretation, the AI-based approach provides objective and reproducible measurements of structural lung changes.
Researchers reported that longitudinal changes in FS over a one-year follow-up period showed stronger statistical significance in evaluating disease progression and prognosis compared with declines in forced vital capacity (FVC), a commonly used clinical indicator in ILD management.
The study is being viewed as an important example of how AI-powered quantitative imaging could evolve from a computer-aided detection tool into a long-term monitoring and treatment response evaluation platform for chronic respiratory diseases.
Interstitial lung disease represents a group of severe pulmonary disorders characterized by inflammation and fibrosis of lung tissue. Because fibrosis is often irreversible, early detection and accurate monitoring of disease progression are considered critical for improving patient outcomes. However, treatment options remain limited, with most currently approved therapies focused on slowing disease progression rather than reversing fibrosis itself.
This unmet clinical demand has fueled rapid growth in the pulmonary fibrosis treatment market. Boehringer Ingelheim’s antifibrotic drug Ofev, for example, has become a blockbuster therapy generating multi-billion-dollar annual sales globally, underscoring the growing commercial importance of ILD and pulmonary fibrosis management.
As pharmaceutical companies accelerate development of next-generation ILD therapies, interest is also increasing in digital biomarkers capable of objectively measuring disease progression and treatment response during clinical trials.
Industry observers say the latest findings highlight the broader transformation underway in the medical AI sector, where imaging AI is increasingly being positioned not only as a diagnostic support tool but also as infrastructure for drug development, longitudinal disease monitoring, and quantitative clinical assessment.
Coreline Soft has been expanding its “Multi-disease Analysis” strategy, which enables simultaneous AI analysis of multiple thoracic conditions—including lung nodules, chronic obstructive pulmonary disease (COPD), cardiovascular abnormalities, and pulmonary fibrosis—from a single low-dose chest CT scan.
The company believes the new study strengthens clinical evidence supporting the use of AI-based quantitative analysis across a wider range of respiratory diseases, including difficult-to-treat fibrotic lung disorders.
Still, experts note that broader adoption of digital biomarkers in routine clinical settings will require additional multi-center validation studies, larger longitudinal datasets, and clearer regulatory frameworks. Questions also remain regarding how reliably AI-generated quantitative metrics can be integrated into real-world treatment decision-making processes.
“The medical AI industry is evolving beyond image interpretation toward predictive analytics and clinical trial infrastructure,” an industry official said. “Collaboration between pharmaceutical companies and AI imaging firms is likely to accelerate further as digital biomarkers become more important in precision medicine and chronic disease management.”
