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

Author:

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

Abstract

AbstractAlthough 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.

Funder

Osaka University

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Reference116 articles.

1. Crevier D (1993) AI: the tumultuous search for artificial intelligence. BasicBooks, New York, NY

2. McCorduck, Pamela (2004) Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1, OCLC 52197627

3. Russell S, Norvig P (2020) Artificial intelligence: a modern approach, 4th edn. Pearson, London, pp 19–53

4. Dhawan AP (2011) Medical image analysis. Wiley, Hoboken, NJ

5. Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10(3):257–273. https://doi.org/10.1007/s12194-017-0406-5

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