ArticleRheumatology international2025
Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.
Article in Rheumatology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.International journal of rheumatic diseases · 2026Pooled it
- Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.BMC rheumatology · 2025Pooled it
- Enhancing Interpretability of AI Performance Metrics in Rheumatology Systematic Reviews.International journal of rheumatic diseases · 2026Article
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Assessment of CDASI scoring by a multimodal large language model: a comparative study with expert assessors.Rheumatology international · 2026Article
- Clinical-psychosocial archetypes predict short-term outcomes in inflammatory arthritis: an unsupervised segmentation study.Clinical rheumatology · 2026Observational
- Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.Rheumatology international · 2026Article
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Global patterns and predictors of initial treatment in early rheumatoid arthritis: insights from a multinational machine learning study.Clinical rheumatology · 2026Article
- Bridging the gap: combining treat-to-target and difficult-to-treat strategies in the management of rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Comparative evaluation of large language model-based AI platforms for radiographic assessment in rheumatoid arthritis.Rheumatology international · 2026Article
- The prediction of a good therapeutic response and outcome: at baseline or after a short term?Clinical rheumatology · 2026Article
- Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.Journal of managed care & specialty pharmacy · 2026Article
- Digital interactions with the pharmaceutical industry: a qualitative focus group study on the perspectives of rheumatology care providers in Germany.BMC rheumatology · 2026Article
- Global variation in the quality of care for rheumatoid arthritis and associated factors.Arthritis research & therapy · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation.Briefings in bioinformatics · 2026Review
- Research Advances on the Mechanism and Diagnosis of Bone Bridging in Ankylosing Spondylitis.Orthopedic research and reviews · 2026Review
- Multi-omics insights into spondyloarthritis and psoriatic arthritis: integrating genomics, transcriptomics, proteomics, and the microbiome for immunological and clinical translation.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.