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Background <p>The integration of artificial intelligence (AI) into musculoskeletal radiology has the potential to revolutionize diagnostic precision and efficiency. At the beginning of the radiological diagnostic workflow, AI algorithms lead to a&#xa0;significant reduction in examination time, for example in image acquisition in MRI.</p> Diagnostics <p>Certain AI algorithms achieve a&#xa0;diagnostic accuracy in fracture detection that is comparable to that of board-certified radiologists and perform time-consuming diagnostic analyses such as joint and axis measurements fully automatically. Generative AI based on large language models (LLM) can contribute to automation in structured reporting, with first clinical applications already available.</p> Challenges <p>While AI algorithms for accelerating MRI acquisition already contribute significantly to efficiency, challenges in the clinical translation of AI algorithms for automated reading support lie primarily in the low number of commercially available AI algorithms that offer a&#xa0;significant clinical value to quality and efficiency in the diagnostic workflow. Accepted key performance indices (KPI) for quantitatively measuring the return on investment (ROI) for the high running costs are also largely unresolved internationally.</p>

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Künstliche Intelligenz in der muskuloskelettalen Radiologie

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Julian Enters,
  • Boj F. Hoppe,
  • Paul Reidler,
  • Michael Ingrisch,
  • Jens Ricke,
  • Lalith Kumar Shiyam Sundar,
  • Clemens Cyran

摘要

Background

The integration of artificial intelligence (AI) into musculoskeletal radiology has the potential to revolutionize diagnostic precision and efficiency. At the beginning of the radiological diagnostic workflow, AI algorithms lead to a significant reduction in examination time, for example in image acquisition in MRI.

Diagnostics

Certain AI algorithms achieve a diagnostic accuracy in fracture detection that is comparable to that of board-certified radiologists and perform time-consuming diagnostic analyses such as joint and axis measurements fully automatically. Generative AI based on large language models (LLM) can contribute to automation in structured reporting, with first clinical applications already available.

Challenges

While AI algorithms for accelerating MRI acquisition already contribute significantly to efficiency, challenges in the clinical translation of AI algorithms for automated reading support lie primarily in the low number of commercially available AI algorithms that offer a significant clinical value to quality and efficiency in the diagnostic workflow. Accepted key performance indices (KPI) for quantitatively measuring the return on investment (ROI) for the high running costs are also largely unresolved internationally.