Practical Applications of AI in MSK Radiology

Review maps AI use in MSK radiology, spanning image acquisition, interpretation, LLMs, workflow integration, cost-effectiveness, and liability.

By
Julie Greenbaum
January 9, 2026
4 min

Conexiant

Overview

A scoping review highlights the integration of AI in musculoskeletal (MSK) radiology, focusing on applications from image acquisition to reporting. Key findings include significant reductions in scan times and improved diagnostic performance, particularly in fracture detection and tumor characterization.

Background

The integration of artificial intelligence (AI) in musculoskeletal radiology is crucial due to the increasing volume of imaging studies and the complexity of conditions treated in this field. AI technologies have the potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. Understanding these applications is essential for radiologists to leverage AI effectively in clinical practice.

Data Highlights

No numerical data provided in the source material.

Key Findings

AI applications in MSK radiology include fracture detection, deep learning reconstruction, and automated classification of lesions.
Studies report scan-time reductions of up to 53% with AI-based reconstruction techniques without compromising diagnostic performance.
AI-assisted radiologists outperform both AI alone and radiologists alone in fracture detection tasks.
Large language models (LLMs) are being explored for automating report generation and improving communication in MSK imaging.
Long-term adoption of AI will depend on cost-effectiveness and seamless integration into existing workflows.

Clinical Implications

Radiologists should consider incorporating AI tools to enhance diagnostic accuracy and efficiency in MSK imaging. Training and guidelines are essential to ensure these technologies are used effectively and safely within clinical workflows.

Conclusion

AI is transforming MSK radiology by improving diagnostic capabilities and operational efficiency. Continued research and integration into clinical practice are necessary to maximize the benefits of these technologies.