Role of Artificial Intelligence in Musculoskeletal Interventions.
Simple Summary: Artificial Intelligence (AI) is a fundamental aspect of an evolving paradigm shift in radiology. This article outlines how AI-based methods are driving changes in diagnostic and interventional musculoskeletal radiology with a wide range of specific applications discussed including pr...
| Published in: | Cancers Vol. 17; no. 10; pp. 1615 - 1629 |
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| Main Authors: | , , , , , |
| Format: | review tables/charts Journal Article |
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MDPI
May2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185481003&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185481003 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: May2025 vid: 17 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 185481003 185481003 185481003 10.3390/cancers17101615 185481003 ppf: 1615 ppct: 14 formats: tig: atl: Role of Artificial Intelligence in Musculoskeletal Interventions. aug: au: Dubey, Anuja Uldin, Hasaam Khan, Zeeshan Panchal, Hiten Iyengar, Karthikeyan P. Botchu, Rajesh affil: Department of Radiology, Healthcare Imaging Centre, Meerut 250001, India sug: subj: Artificial Intelligence Diagnosis, Musculoskeletal Radiography, Computed Individualized Medicine Ultrasonography Biopsy Tomography, X-Ray Computed Fluoroscopy Radiation Injuries Prevention and Control Minimally Invasive Procedures Augmented Reality Radiation Dosage Robotics ab: Simple Summary: Artificial Intelligence (AI) is a fundamental aspect of an evolving paradigm shift in radiology. This article outlines how AI-based methods are driving changes in diagnostic and interventional musculoskeletal radiology with a wide range of specific applications discussed including procedures involving ultrasound, CT, and fluoroscopy. Methods of utilizing AI to optimize the patient and practitioner experience such as feedback systems, dose-optimization, and segmentation algorithms are reviewed. These changes will play a significant role in shaping the rapidly changing landscape of musculoskeletal radiology. Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures for joints, nerves, and tumor-targeted interventions, along with CT-guided biopsies and ablations, and fluoroscopy-guided facet joint and nerve block injections. AI-powered segmentation algorithms, real-time feedback systems, and dose-optimization protocols collectively enable greater precision, operator consistency, and patient safety. In rehabilitation, AI-driven wearables and predictive models facilitate personalized exercise programs that can accelerate recovery and enhance long-term function. While challenges persist—such as data standardization, regulatory hurdles, and clinical adoption—ongoing interdisciplinary collaboration, federated learning models, and the integration of genomic and environmental data hold promise for expanding AI's capabilities. As personalized medicine continues to advance, AI is poised to refine risk stratification, reduce radiation exposure, and support minimally invasive, patient-specific interventions, ultimately reshaping musculoskeletal care from early detection and diagnosis to individualized treatment and rehabilitation. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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