<p>The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging. For image generation, we focused on the clinical application of deep learning reconstruction and the recently emerging MRI-based cortical bone imaging. For automated diagnostic support, we provided an overview of qualitative diagnosis, including classifications essential for daily practice, and quantitative diagnosis, which can serve as imaging biomarkers for treatment decision making and prognosis prediction. Finally, we discussed current issues in the use of AI, the application of AI in the diagnosis of rare diseases, and the role of AI-based diagnostic imaging in preventive medicine as part of our outlook for the future.</p>

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Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

  • Taiki Nozaki,
  • Masahiro Hashimoto,
  • Daiju Ueda,
  • Shohei Fujita,
  • Yasutaka Fushimi,
  • Koji Kamagata,
  • Yusuke Matsui,
  • Rintaro Ito,
  • Takahiro Tsuboyama,
  • Fuminari Tatsugami,
  • Noriyuki Fujima,
  • Kenji Hirata,
  • Masahiro Yanagawa,
  • Akira Yamada,
  • Tomoyuki Fujioka,
  • Mariko Kawamura,
  • Takeshi Nakaura,
  • Shinji Naganawa

摘要

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging. For image generation, we focused on the clinical application of deep learning reconstruction and the recently emerging MRI-based cortical bone imaging. For automated diagnostic support, we provided an overview of qualitative diagnosis, including classifications essential for daily practice, and quantitative diagnosis, which can serve as imaging biomarkers for treatment decision making and prognosis prediction. Finally, we discussed current issues in the use of AI, the application of AI in the diagnosis of rare diseases, and the role of AI-based diagnostic imaging in preventive medicine as part of our outlook for the future.