Download PDF
Semin Musculoskelet Radiol 2026; 30(01): 075-093
DOI: 10.1055/a-2736-1861
Review Article

Strategic Integration of Artificial Intelligence in Musculoskeletal Interventions: Scope, Success, and Pursuit of Personalized Precision Care Models

Authors

  • Amanda Isaac 

    1   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
    2   London Institute for Healthcare Engineering (LIHE), King's College London, London, United Kingdom

  • Michail E. Klontzas 

    3   Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece

  • Nicolas Amoretti 

    4   Radiology Department, CHU Nice Pasteur 2, Nice, France

  • Jaspreet Grewal

    2   London Institute for Healthcare Engineering (LIHE), King's College London, London, United Kingdom

  • Salem Bauones 

    5   Interventional Radiology, King Fahad Medical City, Riyadh, Saudi Arabia

  • Andro Matković 

    6   Clinical Department for Diagnostic and Interventional radiology, University Hospital Merkur, Zagreb, Croatia

  • David Drobny

    1   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

  • Dimitrios Filippiadis 

    7   2nd Department of Radiology, University General Hospital “ATTIKON,” Medical School, National and Kapodistrian University of Athens, Athens, Greece

  • Danoob Dalili 

    8   Southwest London Elective Orthopaedic Centre, Epsom and St Helier Hospitals, Surrey, London, United Kingdom
Further Information
Also available at
   Permissions and Reprints

Abstract

Musculoskeletal image-guided interventions are integral to modern radiologic practices, offering precise, minimally invasive solutions for a range of diagnostic and therapeutic needs. Despite their clinical utility, these interventions are often limited by operator dependency, procedural variability, and a lack of workflow standardization. As artificial intelligence technologies continue to evolve, they offer a significant opportunity to enhance the precision, safety, and efficiency of musculoskeletal interventions.

This review explores the current and emerging applications of artificial intelligence across the procedural continuum of musculoskeletal image-guided interventions. We examine its role in preprocedural planning—including lesion detection, segmentation, and personalized risk stratification—through to intraprocedural guidance with artificial intelligence–assisted navigation, needle trajectory optimization, and robotics, and finally postprocedural evaluation using radiomics, complication prediction, and natural language processing for structured reporting.

We address the critical challenges that limit clinical translation, including the need for external validation, generalizability across diverse patient populations, regulatory clearance, along with ethical considerations such as bias, data governance, and equitable access. We highlight the absence of multicenter trials and open-access datasets tailored to interventional practice. Particular attention is given to the role of artificial intelligence as a decision-support tool rather than a replacement for operator expertise, with emphasis on its potential to enhance training, standardize practice, and expand access to high-quality care.

By highlighting both the practical applications and limitations of current technologies and with health care increasingly shaped by digital innovation, radiologists must engage critically with artificial intelligence technologies to ensure safe, effective, and inclusive deployment. This review synthesizes the current state of artificial intelligence in musculoskeletal interventions and outlines future directions, including the need for interventional artificial intelligence registries, foundation model development, and collaborative translational research. By focusing on practical integration, we aim to support radiologists in leveraging artificial intelligence to enhance procedural accuracy, consistency, and patient outcomes.

Keywords

artificial intelligence - interventions - robotic - personalized medicine - patient safety


Publication History

Received: 21 July 2025

Accepted: 09 October 2025

Article published online:
20 February 2026

© 2026. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA