Artificial intelligence for lung disease quantification in systemic sclerosis-associated interstitial lung disease and other connective tissue disease-associated interstitial lung disease

PURPOSE OF REVIEW: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of disease-related mortality in systemic sclerosis and the connective tissue disease-associated ILD (CTD-ILD) in which artificial intelligence imaging has advanced most rapidly. Visual high-resolution CT (HRCT) scoring is reader-dependent and limits clinical decision-making. This review summarizes clinically relevant artificial intelligence and radiomics publications from approximately the last 18 months, focusing on quantification, diagnosis and classification, response assessment, and prognosis. RECENT FINDINGS: The field has moved from visual-score emulation toward outcome-oriented quantitative imaging biomarkers. In SSc-ILD, deep-learning usual interstitial pneumonia (UIP) probability stratifies FVC decline and long-term survival, while whole-chest quantitative imaging biomarkers extend risk prediction beyond lung involvement alone. Automated SSc-specific segmentation, explainable Goh-equivalent scoring, slice-reduced radiomics, and open datasets have strengthened the methodological base. Parallel work in CTD-ILD, RA-ILD, and IIM-ILD shows that artificial intelligence-derived HRCT parameters correlate with DLCO/TLC, predict mortality, and support disease-pattern classification. In broader fibrosing ILD, qCT definitions of progressive pulmonary fibrosis and clinically meaningful CT thresholds provide the most important conceptual advance for future SSc-ILD trials. SUMMARY: Artificial intelligence-based CT quantification is becoming a credible adjunct for rheumatology and radiology practice, but the most defensible deployment model is human-in-the-loop decision support. Prospective multicenter validation, protocol harmonization, calibration, version control, and integration into multidisciplinary discussion remain essential before artificial intelligence outputs can be used as treatment-triggering biomarkers.