Objective:

To examine current practical applications and perspectives of AI in musculoskeletal (MSK) radiology, highlighting its significance in enhancing diagnostic capabilities.

Key Findings:

• AI has significantly advanced MSK radiology, improving image acquisition and interpretation, with implications for clinical practice.
• Deep learning (DL) techniques have reduced scan times by up to 53% without compromising diagnostic performance, enhancing patient throughput.
• AI tools for fracture detection have shown clinical value, especially in emergency settings, potentially reducing morbidity.
• LLMs are emerging for tasks like automated report generation and classification of MSK disorders, improving communication.
• Successful AI deployment requires integration into existing systems, adherence to regulatory standards, and consideration of clinical workflows.

Interpretation:

AI is reshaping MSK radiology by enhancing diagnostic capabilities and workflow efficiency, while also necessitating careful integration and management of associated risks, including potential information loss.

Limitations:

• Aggressive under-sampling in AI-based reconstruction may lead to information loss, impacting diagnostic accuracy.
• Long-term adoption depends on cost-effectiveness and integration into clinical practices, which must be continuously evaluated.

Conclusion:

AI should serve as an assistive tool to enhance radiologist expertise, with a focus on clear guidelines, performance transparency, and ongoing research to ensure safe adoption.