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[Application of Artificial Intelligence in Radiology].

Atsushi Teramoto1

  • 1Faculty of Radiological Technology, School of Health Sciences, Fujita Health University.

Gan to Kagaku Ryoho. Cancer & Chemotherapy
|March 28, 2019
PubMed
Summary

Artificial intelligence (AI) and deep learning show promise in radiology for improving lesion detection and analysis. This paper introduces an AI-powered decision support system for lung cancer diagnosis.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Artificial intelligence (AI) is an advanced information technology gaining traction across various fields.
  • In radiology, AI technologies are increasingly integrated into computer-aided diagnosis (CADx) for tasks like lesion detection and analysis.
  • Deep learning, a subset of AI, demonstrates superior performance over conventional methods in medical image analysis.

Purpose of the Study:

  • To define fundamental terms related to AI technology.
  • To provide an overview of current domestic and international AI research in radiology.
  • To introduce a novel AI-based decision support system specifically developed for lung cancer diagnosis.

Main Methods:

  • Review of AI terminology and global research activities in medical imaging.

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  • Development and presentation of a deep learning-based decision support system.
  • Application of the system to lung cancer diagnosis.

    • Main Results:

    • AI, particularly deep learning, offers significant advancements in radiological computer-aided diagnosis.
    • The developed decision support system aims to enhance the accuracy and efficiency of lung cancer diagnosis.
    • The study lays groundwork for further research and application of AI in oncologic imaging.

    Conclusions:

    • AI technologies, especially deep learning, are revolutionizing diagnostic capabilities in radiology.
    • The presented decision support system represents a significant step towards AI-integrated lung cancer diagnostics.
    • Further research and development are warranted to fully leverage AI's potential in medical diagnosis.