ReviewNature reviews. Rheumatology2024
Deep learning in rheumatological image interpretation.
Review in Nature reviews. Rheumatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 20 citations in OpenAlex.
- Performance of an artificial intelligence model compared with multiple human experts in scoring synovitis and osteophyte severity on joint ultrasound images.EULAR rheumatology open · 2026Article
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- A foundational architecture for AI agents in healthcare.Cell reports. Medicine · 2025Review
- Patterns of Shared Variation in Knee Ultrasound for Osteoarthritis: A Machine Learning Approach.Osteoarthritis imaging · 2025Article
- Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.Scientific reports · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 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
- From Big Data to AI-Driven Decisions in Obstructive Sleep Apnea: A Narrative Review Integrating the DDPP Framework.Nature and science of sleep · 2025Review
- Review
- Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs.Frontiers in medicine · 2024Article
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
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
Indexed as
Identifiers
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.