ReviewCurrent reviews in musculoskeletal medicine2025
Artificial Intelligence Applications in Musculoskeletal Imaging.
Review in Current reviews in musculoskeletal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- A Hybrid Automatic Model for Circle Detection in X-Ray Imagery: A Case Study on Hip Prosthesis Wear.Bioengineering (Basel, Switzerland) · 2026Article
- AI-Powered Lateral DEXA Morphometry for Integrated Evaluation of Thoracic Kyphosis and Bone Density Assessment in Patients with Axial Spondyloarthritis.Life (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThe demand for AI-driven solutions in musculoskeletal (MSK) imaging has risen alongside the surge in orthopedic imaging studies, reflecting the need for tools that enhance diagnostic accuracy, reduce healthcare costs, and alleviate physician workload. This review explores recent applications of AI-particularly computer vision and deep learning (DL)-in MSK imaging, from trauma and surgery to specialized and point-of-care technologies. The review also highlights existing challenges and limitations hindering the integration of these tools into clinical practice. RECENT
findingsAI applications are abundant in MSK imaging, with DL models showing remarkable versatility and success across multiple use cases. These include but are not limited to fracture detection, segmentation for preoperative planning, surgical navigation and tracking, tumor detection and classification, pediatric bone age estimation, and bone density measurement. Specialized use cases also target injury detection in sports medicine, and AI has been integrated into point-of-care technologies, such as motion-monitoring systems, underscoring AI's broad potential to improve diagnostic accuracy, reduce interpretation times, and increase efficiency. AI has shown promise in transforming MSK imaging, suggesting improvements in diagnostic performance, speed, and cost-efficiency. Despite research advances, challenges remain in deploying AI in real-world clinical settings, where model generalizability, data quality, and high computational demands pose obstacles. However, recent developments in AI, including the rise of adaptable foundation models and advancements in model efficiency, offer promising solutions that may accelerate the integration of AI into clinical workflows, bringing the field closer to realizing the full potential of AI in patient care.
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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.