New trend in artificial intelligence-based assistive technology for thoracic imaging.

Journal: La Radiologia medica
Published Date: Aug 28, 2023

Abstract

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Authors

  • Masahiro Yanagawa
    Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Rintaro Ito
    Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
  • Taiki Nozaki
    Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-0016, Japan.
  • Tomoyuki Fujioka
    Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
  • Akira Yamada
    Department of Radiology, Shinshu University School of Medicine, Japan.
  • Shohei Fujita
    Department of Radiology, Juntendo University School of Medicine.
  • Koji Kamagata
  • Yasutaka Fushimi
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.
  • Takahiro Tsuboyama
    From the Department of Radiology, Osaka University Graduate School of Medicine.
  • Yusuke Matsui
    Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan.
  • Fuminari Tatsugami
    Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
  • Mariko Kawamura
    Department of Radiology, Nagoya University Graduate School of Medicine.
  • 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. ai.labo.ocu@gmail.com.
  • Noriyuki Fujima
    Department of Radiology, Boston Medical Center, Boston University School of Medicine, FGH Building, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.
  • Takeshi Nakaura
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.). Electronic address: kff00712@nifty.com.
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Shinji Naganawa
    Department of Radiology, Nagoya University Graduate School of Medicine.

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