Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality. Current assessment tools, such as the modified Rodnan skin score, pulmonary function tests, and nailfold capillaroscopy, are limited by subjectivity and interobserver variability. Artificial intelligence (AI) is reshaping this landscape. Machine learning, deep learning, and radiomics have shown potential to enhance disease phenotyping, risk stratification, and imaging quantification in SSc. AI-driven high-resolution computed tomography analysis enables automated fibrosis segmentation and radiomic risk modeling in SSc-ILD. Computer vision approaches applied to nailfold capillaroscopy achieve expert-level agreement for microvascular staging, whereas emerging digital tools aim to quantify Raynaud phenomenon dynamics. In skin disease, AI-assisted ultrasonography, optical imaging, and histopathology provide reproducible quantification of dermal remodeling. However, most applications remain exploratory, based on small or single-center data sets with limited external validation. Challenges such as data heterogeneity, annotation burden, and the "small data" constraints of rare diseases limit immediate clinical translation. This narrative review critically examines current AI applications in SSc, highlighting methodologic limitations, translational readiness, and future directions toward robust, multicenter, and ethically governed implementation.
© 2026 The Author(s). Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology.