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<p>Structured reporting in knee MRI represents a transformative advancement in musculoskeletal radiology, promising enhanced clarity and consistency in evaluating the knee’s complex anatomy—menisci, ligaments, cartilage, and bone. This review article explores the foundations, evolution, and clinical applications of structured reporting, with a special focus on its role in knee MRI. Furthermore, we examine different studies highlighting the benefits of structured reporting, like improved clinician communication and support for emerging artificial intelligence (AI) tools, while also addressing challenges like balancing structure and a certain amount of narrative flexibility. Frameworks such as anatomical versus tissue-based approaches are explored, and insights into possible best practices are offered. The outlook is promising with AI-driven automation, natural language processing, and patient-centric innovations, driven by global standardization efforts from radiological societies. Structured reporting in knee MRI invites further exploration to unlock its full potential, ultimately leading to improved patient care.</p>

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Structured reporting in knee MRI

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Marco Peter,
  • Tobias Johannes Dietrich,
  • Roman Guggenberger,
  • Anna L. Falkowski,
  • Tim Steffen Fischer

摘要

Structured reporting in knee MRI represents a transformative advancement in musculoskeletal radiology, promising enhanced clarity and consistency in evaluating the knee’s complex anatomy—menisci, ligaments, cartilage, and bone. This review article explores the foundations, evolution, and clinical applications of structured reporting, with a special focus on its role in knee MRI. Furthermore, we examine different studies highlighting the benefits of structured reporting, like improved clinician communication and support for emerging artificial intelligence (AI) tools, while also addressing challenges like balancing structure and a certain amount of narrative flexibility. Frameworks such as anatomical versus tissue-based approaches are explored, and insights into possible best practices are offered. The outlook is promising with AI-driven automation, natural language processing, and patient-centric innovations, driven by global standardization efforts from radiological societies. Structured reporting in knee MRI invites further exploration to unlock its full potential, ultimately leading to improved patient care.