{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:13:27Z","timestamp":1778166807488,"version":"3.51.4"},"reference-count":62,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis Care &amp;amp; Research"],"abstract":"<jats:p>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\u2010driven high\u2010resolution computed tomography analysis enables automated fibrosis segmentation and radiomic risk modeling in SSc\u2010ILD. Computer vision approaches applied to nailfold capillaroscopy achieve expert\u2010level agreement for microvascular staging, whereas emerging digital tools aim to quantify Raynaud phenomenon dynamics. In skin disease, AI\u2010assisted ultrasonography, optical imaging, and histopathology provide reproducible quantification of dermal remodeling. However, most applications remain exploratory, based on small or single\u2010center data sets with limited external validation. Challenges such as data heterogeneity, annotation burden, and the \u201csmall data\u201d 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.<\/jats:p>\n                  <jats:p>\n                    <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                      <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/acr80068-gra-0001-m.png\">\n                        <jats:alt-text>image<\/jats:alt-text>\n                      <\/jats:graphic>\n                    <\/jats:boxed-text>\n                  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