Japanese Radiology 2025 Updates.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Oct 9, 2025

Abstract

This review provides a comprehensive overview of recent transformative advancements in diagnostic imaging that position Japan at the forefront of radiological innovation. We highlight pivotal innovations that enhance diagnostic capabilities and redefine clinical workflows. The article begins with upright multidetector computed tomography (MDCT), a groundbreaking technology offering novel insights into posture-dependent anatomical and physiological changes. We then explore significant progress in breast and gynecologic imaging, including advancements in artificial intelligence computer-aided (AI-CAD) synthesized mammograms, automated breast ultrasound (ABUS), and abbreviated MRI protocols. These innovations address unique regional challenges in early cancer detection. Significant innovations in abdominal radiology, spanning advanced CT (including photon-counting detector CT), accelerated MRI, and AI applications, are also discussed. The review further delves into glymphatic system research, where advanced MRI techniques, particularly DTI-ALPS, are unraveling new insights into brain waste clearance and neurological disorders. Finally, we examine the future of Japanese radiology through the lens of AI, with a focus on Large Language Models (LLMs). We discuss their growing role in diagnostic support, report generation, and information extraction, as well as important societal and ethical considerations. These collective advancements underscore Japan's dynamic contributions to radiological innovation, poised to significantly impact global healthcare practices by improving disease detection, optimizing workflows, and extending healthy life expectancy in an aging society.

Authors

  • Mami Iima
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Graduate School of Medicine, Kyoto, Japan.
  • Tsukasa Saida
    Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
  • Yoshitake Yamada
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan. Electronic address: [email protected].
  • Ryo Kurokawa
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Daiju Ueda
    Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan. [email protected].
  • Maya Honda
    From the Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan (M.I.); Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.I., M.K., M.H., Y.N.); A.I. System Research, Kyoto, Japan (R.M.); Kyoto University Faculty of Medicine, Kyoto, Japan (K.T., T.Y.); Department of Diagnostic Radiology, Kyoto City Hospital, Kyoto, Japan (A.M.); Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan (M.H.); e-Growth, Kyoto, Japan (K.I.); and Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.T.).
  • Kentaro Nishioka
    Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Rintaro Ito
    Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
  • Keitaro Sofue
    Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe City, Hyogo 650-0017, Japan.
  • Shinji Naganawa
    Department of Radiology, Nagoya University Graduate School of Medicine.

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