To analyse the types and applications of artificial intelligence (AI) technologies to predict treatment response in rheumatoid arthritis (RA) and spondyloarthritis (SpA). A comprehensive search in Medline, Embase, and Cochrane databases (up to August 2024) identified studies using AI to predict treatment response in RA and SpA. Data on study design, AI methodologies, data sources, and outcomes were extracted and synthesized. Findings were summarized descriptively. Of the 4257 articles identified, 89 studies met the inclusion criteria (74 on RA, 7 on SpA, 4 on Psoriatic Arthritis and 4 a mix of them). AI models primarily employed supervised machine learning techniques (e.g., random forests, support vector machines), unsupervised clustering, and deep learning. Data sources included electronic medical records, clinical biomarkers, genetic and proteomic data, and imaging. Predictive performance varied by methodology, with accuracy ranging from 60 to 70% and AUC values between 0.63 and 0.92. Multi-omics approaches and imaging-based models showed promising results in predicting responses to biologic DMARDs and JAK inhibitors but methodological heterogeneity limited generalizability. AI technologies exhibit substantial potential in predicting treatment responses in RA and SpA, enhancing personalized medicine. However, challenges such as methodological variability, data integration, and external validation remain. Future research should focus on refining AI models, ensuring their robustness across diverse patient populations, and facilitating their integration into clinical practice to optimize therapeutic decision-making in rheumatology.

A clinical guideline is a document with the aim of guiding decisions based on evidence regarding diagnosis, management and treatment in specific areas of healthcare. Specific to rheumatic and musculoskeletal diseases (RMDs), adherence to clinical guidelines recommendations impacts the outcomes of people with these diseases. However, currently, the implementation of recommendations is less than optimal in rheumatology. The WHO has described the implementation of evidence-based recommendations as one of the greatest challenges facing the global health community and has identified the importance of scaling up these recommendations. But closing the evidence-to-practice gap is often complex, time-consuming and difficult. In this context, the implementation science offers a framework to overcome this scenario. This article describes the principles of implementation science to facilitate and optimise the implementation of clinical recommendations in RMDs. Embedding implementation science methods and techniques into recommendation development and daily practice can help maximise the likelihood that implementation is successful in improving the quality of healthcare and healthcare services.