Artificial Intelligence in Personalizing Rheumatoid Arthritis Treatment: A Scoping Review

Background: This scoping review summarizes the types and applications of Artificial Intelligence (AI) technologies for predicting responses to Rheumatoid Arthritis (RA) treatment. Methods: A detailed search of PubMed and Google Scholar was performed from January, 2021 to August, 2025 to identify cohort studies using AI for predicting RA treatment responses. Information on the study design, AI strategies, data sources, and model performance was mined and descriptively synthesized. Results: A total of 805 articles were initially identified, of which 46 met the inclusion criteria. Logistic regression, support vector machines, random forest, and ensemble models were the most commonly used AI models. Various data sources, such as clinical parameters and genomic, transcriptomic, proteomic, and electronic health record data, have also been used. Most of the models showed moderate-to-high discriminative performance (AUROC > 0.80). Discussion: This review reveals the possibilities of AI in predicting RA treatment responses. Models indicated high accuracy, particularly when multi-omics data were used. Nevertheless, there was considerable variation in the performance of models in different studies as they used different data sources, outcome definitions, and validation methods. These drawbacks restrict the comparability and generalizability of the existing models, and real-world issues of interpretability and applicability remain. Conclusion: AI has demonstrated significant potential for predicting responses to RA treatment and assisting in individualized therapy. Further studies are needed focusing on standardized performance assessment, multi-center external validation, and multi-omics combined data to create clinically reliable and generalizable AI models.