ReviewArthritis & rheumatology (Hoboken, N.J.)2022
Artificial Intelligence and Deep Learning for Rheumatologists.
Review in Arthritis & rheumatology (Hoboken, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.European radiology · 2025Pooled it
- Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.RMD open · 2023Pooled it
- Synovial microenvironment and fluorescence imaging for early rheumatoid arthritis diagnosis.Journal of pharmaceutical analysis · 2026Review
- Automated high-resolution CT analysis outperforms visual assessment in predicting interstitial lung disease progression in SSc.Rheumatology (Oxford, England) · 2026Article
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- A large-scale vision foundation model for musculoskeletal radiographs.NPJ digital medicine · 2026Article
- Artificial Intelligence (AI) in rheumatology: a comparative evaluation of the ChatGPT and DeepSeek application.BMC rheumatology · 2026Article
- Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.Frontiers in medicine · 2026Review
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
- Development of machine learning models for chronic fatigue prediction in granulomatosis with polyangiitis.Advances in rheumatology (London, England) · 2025Article
- Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.Scientific reports · 2025Article
- Artificial intelligence-based Raynaud's quantification index (ARTIX): an objective mobile-based tool for patient-centered assessment of Raynaud's phenomenon.Arthritis research & therapy · 2025Article
- Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures.Journal of orthopaedic surgery and research · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints.The Journal of international medical research · 2025Article
- Large language models and rheumatology: are we there yet?Rheumatology advances in practice · 2025Review
- Deep Learning Classification of Rheumatoid Arthritis in Hand Radiographs Interpretability Insights and Web Application.ImmunoTargets and therapy · 2025Article
- Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases.PeerJ. Computer science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.
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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.