ReviewEuropean radiology experimental2024
AI applications in musculoskeletal imaging: a narrative review.
Review in European radiology experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed, 5 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Whole-body MRI for staging and follow-up of primary musculoskeletal tumours: a systematic review.The British journal of radiology · 2026Pooled it
- Pooled it
- Automated detection of bone lesions using CT and MRI: a systematic review.La Radiologia medica · 2024Pooled it
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- Artificial intelligence for postoperative plain radiographic assessment after reverse total shoulder arthroplasty: current evidence, clinical readiness, and limitations.Clinics in shoulder and elbow · 2026Article
- Artificial Intelligence in Orthopaedics: Current Evidence and Clinical Translation Across the Patient Care Pathway.Journal of clinical medicine · 2026Review
- Review
- The invisible hip: radiographic "misses" in hip-spine syndrome and the diagnostic disparity between orthopedic surgeons and neurosurgeons.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Evaluating the Performance of ChatGPT-4V in Detecting Inflammatory Magnetic Resonance Imaging Findings of Sacroiliitis: Potentials, Challenges, and Limitations.Journal of imaging informatics in medicine · 2026Article
- The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.European radiology experimental · 2026Article
- Utilization and perceptions of artificial intelligence in pain medicine practice: An international pain and spine intervention society survey-based analysis.Interventional pain medicine · 2026Article
- Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction.European radiology experimental · 2026Article
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- Comparative evaluation of large language model-based AI platforms for radiographic assessment in rheumatoid arthritis.Rheumatology international · 2026Article
- Review
- Article
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.
Indexed as
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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.