How Can Artificial Intelligence Help Avoid Mistakes in Musculoskeletal Imaging?

Area of Science:

• Orthopedic imaging
• Medical artificial intelligence
• Radiology workflow optimization

Background:

• Musculoskeletal imaging is crucial for orthopedics but prone to errors.
• Increasing demand and complexity exacerbate these diagnostic challenges.
• Current limitations include subjective assessments and measurement variability.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) in mitigating errors in musculoskeletal imaging.
• To highlight AI applications across the entire imaging workflow.
• To assess AI's role in improving diagnostic accuracy, efficiency, and reproducibility.

Main Methods:

• Review of current AI applications in musculoskeletal imaging, including deep learning and large language models.
• Analysis of AI's impact on various stages: exam requests, protocol optimization, artifact reduction, and interpretation.
• Examination of AI's utility across multiple imaging modalities (MR, radiography, CT, ultrasound).

Main Results:

• AI demonstrates potential in reducing interpretive and noninterpretive errors in musculoskeletal imaging.
• Applications span all major imaging modalities, addressing common pitfalls.
• AI tools, particularly large language models, enhance report clarity and patient communication.

Conclusions:

• Artificial intelligence offers a transformative opportunity to improve musculoskeletal imaging quality and patient safety.
• Human-machine collaboration is key to leveraging AI's full potential in clinical practice.
• Ongoing integration of AI promises enhanced diagnostic accuracy and workflow efficiency in orthopedics.