AI CT quantification in SSc-ILD shifts from visual scoring to outcome-based biomarkers

AI-based CT quantification in SSc-ILD now moves beyond visual scoring to outcome-oriented biomarkers; deep-learning UIP probability stratifies FVC decline and survival, supporting human-in-the-loop decision support.

• Quantitative CT definitions of progressive pulmonary fibrosis and clinically meaningful CT thresholds provide a conceptual advance for future SSc-ILD trials.
• Automated systemic sclerosis-specific segmentation and explainable Goh-equivalent scoring strengthen methodological foundations.
• Prospective multicenter validation and integration into multidisciplinary discussion remain essential before AI outputs become treatment-triggering biomarkers.

Automated summary

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