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Objectives: Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management., Methods: We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias., Results: Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares., Conclusion: NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Objectives: Autologous hematopoietic stem cell transplantation (AHSCT) has been shown to improve long-term survival for early diffuse progressive SSc compared with CYC. CYC, however, does not provide a long-term benefit in SSc. The combination of MMF and rituximab is a potent alternative regimen. We aimed to retrospectively compare the outcomes of SSc patients who underwent AHSCT to patients who met the eligibility criteria for AHSCT but received upfront combination therapy with MMF and rituximab., Methods: Repeated assessments of modified Rodnan Skin Score (mRSS), forced vital capacity (FVC), and diffusing capacity (DLCO) values were conducted. Clinical improvement was defined as an mRSS decrease >25% or an FVC increase >10%. Event-free survival (EFS) was defined in the absence of persistent major organ failure or death., Results: Twenty-one SSc patients in the combination therapy group were compared with 16 in the AHSCT group. Age, sex and disease duration were similar between the two groups. Clinical improvement at 12 months was seen in 18 (86%) patients in the combination group compared with 13 (81%) in the AHSCT group (P = 0.7). The hazard ratio for EFS at 24 months favoured the combination group (HR = 0.09, P = 0.04). During follow-up, both groups exhibited a significant and comparable reduction in mRSS and an increase in FVC values at each time interval up to 24 months., Conclusion: MMF and rituximab compared with AHSCT in SSc patients eligible for AHSCT resulted in similar skin and lung clinical improvement with a better safety profile at 24 months., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)