ReviewFrontiers in immunology2024
Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.
Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular mechanisms and risk factors in rheumatoid arthritis: a comprehensive review.Inflammopharmacology · 2026Pooled it
- Predicting the risk of bone erosion in rheumatoid arthritis using a SHAP-based interpretable machine learning model.Clinical rheumatology · 2026Article
- Multi-Omics-Driven Insights into Cancer Biology and Therapeutic Targeting.AAPS PharmSciTech · 2026Review
- Synovial microenvironment and fluorescence imaging for early rheumatoid arthritis diagnosis.Journal of pharmaceutical analysis · 2026Review
- Artificial Intelligence in Rheumatology: A Comprehensive Bibliometric Analysis and Current Scientific Mapping Research.Mediterranean journal of rheumatology · 2026Review
- An interpretable machine learning tool for rheumatoid arthritis screening: integrating novel cellular morphological parameters with routine blood count indices.Clinical rheumatology · 2026Article
- Article
- Bridging the gap: combining treat-to-target and difficult-to-treat strategies in the management of rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation.Briefings in bioinformatics · 2026Review
- Perceptions of healthcare professionals in rheumatology on the use of prediction models in clinical practice.Rheumatology advances in practice · 2026Article
- Immunological heterogeneity in rheumatoid arthritis: challenges in early-stage stratification, non-response to targeted therapy, and the restoration of immune tolerance.Frontiers in immunology · 2026Review
- AdaBoost-based differential diagnosis of gouty arthritis and rheumatoid arthritis: model construction, temporal validation, and SHAP visualization.Frontiers in medicine · 2026Article
- Enhancing the Diagnosis of Behçet's Disease Using Machine Learning: A Comparative Study on Clinical Data From Saudi Arabia.International journal of telemedicine and applications · 2026Article
- Precision medicine in rheumatoid arthritis: advances and clinical applications of multi-omics biomarkers.Frontiers in immunology · 2026Review
- Identification of diagnostic genes in rheumatoid arthritis using integrated bioinformatics, machine learning, and experimental validation.Frontiers in medicine · 2026Article
- Machine Learning-Based Analysis of Serum Interleukin-39 and Interleukin-40 Levels for Differentiating Rheumatoid Arthritis and Systemic Lupus Erythematosus.Medeniyet medical journal · 2025Article
- Calibrated, explainable machine learning on routine laboratory data to characterize diagnostic assignment patterns in rheumatic diseases: a retrospective study of 12,085 patients.BMC rheumatology · 2025Article
- ANAs and triple positivity effect on disease activity and sustained remission in rheumatoid arthritis: a retrospective real-world machine learning approach.Clinical rheumatology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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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.