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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.