ReviewNature reviews. Rheumatology2022
Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.
Review in Nature reviews. Rheumatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis 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
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Evaluating potential for AI automation of quantitative and semi-quantitative MRI scoring in arthritis, especially at the knee: a systematic literature review.Skeletal radiology · 2025Pooled it
- Emerging therapies for knee osteoarthritis.Nature reviews. Rheumatology · 2026Review
- Janus porphyrin-stabilized phase-transition theranostics for multimodal imaging-guided synergistic cancer therapy.Materials today. Bio · 2026Article
- Osteoarthritis: Epidemiology, Diagnosis, and Treatment.MedComm · 2026Review
- Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.Journal of imaging informatics in medicine · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Comparative evaluation of large language models on multiple-choice and image-based rheumatology questions.Rheumatology international · 2025Article
- Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.Scientific reports · 2025Article
- Intelligent microstructure materials for diagnosis and treatment of osteoarthritis: progress and AI-enpowered future.Bone research · 2025Review
- Nanoparticle-enabled molecular imaging diagnosis of osteoarthritis.Materials today. Bio · 2025Review
- Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence.Journal of orthopaedic translation · 2025Article
- Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation.Frontiers in medicine · 2025Review
- Case Report: Osteomyelitis in a giant panda (Frontiers in veterinary science · 2025Article
- Minimally Invasive Therapies for Knee Osteoarthritis.Journal of personalized medicine · 2024Review
- Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.RMD open · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications.Bone research · 2024Review
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- The role of imaging in disentangling the enigma of osteoarthritis.Skeletal radiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.
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.