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Review
. 2021 Jan 28:7:614258.
doi: 10.3389/fmolb.2020.614258. eCollection 2020.

Artificial Intelligence for the Future Radiology Diagnostic Service

Seong K Mun  1 , Kenneth H Wong  1 , Shih-Chung B Lo  1 , Yanni Li  1 , Shijir Bayarsaikhan  1
Affiliations
Review

Artificial Intelligence for the Future Radiology Diagnostic Service

Seong K Mun et al. Front Mol Biosci. .

Abstract

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.

Keywords: CNN; artificial intelligence; integrated diagnostics; productivity; radiology; workflow.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Digital Transformation of Radiology with 3 Categories of Application of ML/AI.
See this image and copyright information in PMC

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