ReviewNature reviews. Rheumatology2021
An introduction to machine learning and analysis of its use in rheumatic diseases.
Review in Nature reviews. Rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 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
46 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Exhaled breath volatile organic compounds (VOCs) detection methods: GC-MS versus eNose in COPD diagnosis-a systematic review and meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.RMD open · 2023Pooled it
- Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.Clinical rheumatology · 2026Article
- Radiograph-Based Deep Learning Model to Support Finger Joint Selection for Ultrasound Examination in Rheumatoid Arthritis.Diagnostics (Basel, Switzerland) · 2026Article
- Automated grading and diagnosis of sacroiliitis on CT images using a 3D convolutional neural network: a multicenter retrospective study.Scientific reports · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- HERC5, IFI6, IFIT3, and OASL as potential diagnostic biomarkers for systemic lupus erythematosus: an integrated bioinformatics, machine learning and clinical validation.Clinical rheumatology · 2026Article
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
- Machine learning integrating immune-related cells, immunoglobulins, and immuno-inflammatory markers identifies risk factors for pulmonary comorbidities in ankylosing spondylitis: stratified analyses by chronic obstructive pulmonary disease and pulmonary nodules.Frontiers in immunology · 2026Article
- Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.Annals of medicine · 2025Article
- Management and treatment of early rheumatoid arthritis, process and barriers among rheumatologists in Spain: a Delphi consensus.BMC rheumatology · 2025Article
- Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.Clinical and experimental nephrology · 2025Article
- Insulin Resistance Indices Predict Mortality in Cardiovascular Disease: A Large-Scale NHANES Study With Machine Learning Validation.Food science & nutrition · 2025Article
- Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study.Insights into imaging · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.