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A Novel Application of Musculoskeletal Ultrasound Imaging
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Artificial Intelligence Applications in Musculoskeletal Imaging.

M Moein Shariatnia1, Sara Bagherieh2, Farbod Semnani3

  • 1School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Current Reviews in Musculoskeletal Medicine
|October 31, 2025
PubMed
Summary

Artificial intelligence (AI) and deep learning (DL) are transforming musculoskeletal (MSK) imaging by improving diagnostic accuracy and efficiency. Challenges in clinical integration persist, but advancements in AI offer solutions for broader adoption in patient care.

Keywords:
Artificial intelligenceAutomated image analysisMusculoskeletal imagingOrthopedic surgery

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Area of Science:

  • Orthopedic Imaging
  • Artificial Intelligence
  • Computer Vision
  • Deep Learning

Background:

  • The increasing volume of orthopedic imaging studies necessitates advanced tools for enhanced diagnostic accuracy, cost reduction, and physician workload management.
  • Artificial intelligence (AI), particularly computer vision and deep learning (DL), presents a growing solution set for musculoskeletal (MSK) imaging challenges.

Purpose of the Study:

  • To review recent applications of AI, including computer vision and DL, in MSK imaging across various subspecialties.
  • To identify current challenges and limitations hindering the clinical integration of AI in MSK imaging.

Main Methods:

  • Review of recent literature on AI applications in musculoskeletal imaging.
  • Focus on computer vision and deep learning techniques.

Main Results:

  • AI, especially DL, demonstrates versatility in fracture detection, segmentation, surgical navigation, tumor analysis, bone age estimation, and density measurement.
  • AI applications extend to sports medicine and point-of-care technologies, improving diagnostic accuracy, reducing interpretation times, and enhancing efficiency.
  • Despite promising results, challenges like model generalizability, data quality, and computational demands impede real-world clinical deployment.

Conclusions:

  • AI holds significant potential to revolutionize MSK imaging, offering improvements in diagnostic performance, speed, and cost-effectiveness.
  • Advancements in AI, such as foundation models and improved efficiency, are paving the way for accelerated clinical integration.
  • Overcoming deployment challenges is key to realizing the full potential of AI in enhancing patient care within MSK imaging.