Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. In parallel, generative AI, driven by large language models, is becoming a powerful tool for optimising the research workflow by supporting with content generation, literature review automation and clinical decision support. This review explores the current applications and future potential of both discriminative and generative AI in rheumatology. It also highlights the challenges posed by these technologies, such as ethical concerns and the need for rigorous validation and regulatory oversight. The integration of AI in rheumatology promises substantial advancements but requires a balanced approach to optimise benefits and minimise potential possible downsides.

Sjögren’s syndrome is one of the most prevalent autoimmune diseases after rheumatoid arthritis, with a preference for middle age, and is characterised by exocrine glandular involvement leading to xerostomia and xerophthalmia. It can have systemic implications with vascular, neurological, renal, and pulmonary involvement, and in some cases, it may evolve to non-Hodgkin’s lymphoma. For a long time, B- and T-lymphocytes have been the focus of research and have been considered key players in Sjögren’s syndrome pathogenesis and evolution. With the development of new technologies, including omics, more insights have been found on the different signalling pathways that lead to inflammation and activation of the immune system. New evidence indicates that a third actor linking innate and adaptive immunity plays a leading role in the Sjögren’s syndrome play: the monocyte. This review summarises the recent insights from transcriptomic, proteomic, and epigenetic studies that help us to understand more about the Sjögren’s syndrome pathophysiology and redefine the involvement of monocytes in this disease.

Excessive noise pollution is often a problem for neonatal nurseries. Noise pollution involves not only noise but also vibrations. The main difference between them is that noise can be heard, and vibrations are felt. The human ear cannot detect waves outside the range of 20 Hz–20 KHz. Waves from 0 Hz to 80–100 Hz should be considered vibrations. Both can be transmitted to the neonate through the incubator’s operational mechanisms and other noise sources. Neonatal units’ noise is well studied but very little is known about vibration. This entry focuses on the importance of vibrations reaching the inside of incubators in neonatal nurseries.

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. image