ReviewRheumatology advances in practice2020
Applied machine learning and artificial intelligence in rheumatology.
Review in Rheumatology advances in practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 5 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
82 citing papers in PubMed, 5 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
- Rheumatoid Arthritis referral criteria: systematic review of the literature.BMC rheumatology · 2026Pooled it
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Pooled it
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Efficacy of Risankizumab across distinct PsA phenotypes identified with machine learning analytics using data from biologic DMARD-Naïve patients in two phase 3 clinical trials.Arthritis research & therapy · 2025Trial
- Trial
- Trial
- Accuracy and usability of a diagnostic decision support system in the diagnosis of three representative rheumatic diseases: a randomized controlled trial among medical students.Arthritis research & therapy · 2021Trial
- Integration of Clinical, Cytokine, and Ultrasound Data Using Machine Learning Reveals a Multidimensional Inflammatory Signature in Polymyalgia Rheumatica.Journal of personalized medicine · 2026Article
- Tree-based machine learning models recover rule-based flare labels from virtual symptom diaries in pediatric Behçet's disease: an in silico proof-of-concept study.Rheumatology international · 2026Article
- Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET.EULAR rheumatology open · 2026Article
- Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort.Lupus science & medicine · 2026Article
- Polymyalgia rheumatica patients presenting with atypical clinical features still exhibit characteristicEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Application of machine learning in the research progress of post-kidney transplant rejection.World journal of transplantation · 2026Review
- Artificial Intelligence (AI) in rheumatology: a comparative evaluation of the ChatGPT and DeepSeek application.BMC rheumatology · 2026Article
- A Translational Roadmap for Neurological Nonsense Mutation Disorders.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- A decision tree-based algorithm for structured risk stratification of rare rheumatic diseases in a tertiary referral setting.Frontiers in medicine · 2026Article
- Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient's opinion and the rheumatologist's empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.
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What OpenQuestion 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 OpenQuestion graph.