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 has shown potential to enhance disease phenotyping, risk stratification, and imaging quantification in SSc. AI‐driven high‐resolution CT analysis enables automated fibrosis segmentation and radiomic risk modelling in SSc‐ILD. Computer vision approaches applied to nailfold capillaroscopy achieve expert‐level agreement for microvascular staging, while emerging digital tools aim to quantify Raynaud's phenomenon dynamics. In skin disease, AI‐assisted ultrasound, optical imaging, and histopathology provide reproducible quantification of dermal remodelling. However, most applications remain exploratory, based on small or single‐centre datasets with limited external validation. Challenges including 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 methodological limitations, translational readiness, and future directions toward robust, multicentre, and ethically governed implementation. image
Santos et al. (Mon,) studied this question.