Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Jun 28, 2020; 1(1): 19-30
Published online Jun 28, 2020. doi: 10.35711/aimi.v1.i1.19
Artificial intelligence in pancreatic disease
Bang-Bin Chen
Bang-Bin Chen, Department of Medical Imaging, National Taiwan University Hospital, Taipei 10016, Taiwan
Bang-Bin Chen, Department of Radiology, College of Medicine, National Taiwan University, Taipei 10016, Taiwan
Author contributions: Chen BB wrote and revised the manuscript.
Supported by grants from the Ministry of Science and Technology (Taiwan), No. 104-2314-B-002-080-MY3 and No. 107-2314-B-002-102-MY3.
Conflict-of-interest statement: No conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Bang-Bin Chen, MD, Associate Professor, Department of Medical Imaging, National Taiwan University College of Medicine and Hospital, No. 7, Chung-Shan South Road, Taipei 10016, Taiwan.
Received: June 9, 2020
Peer-review started: June 9, 2020
First decision: June 15, 2020
Revised: June 18, 2020
Accepted: June 20, 2020
Article in press: June 20, 2020
Published online: June 28, 2020
Core Tip

Core tip: The integration of radiomics, clinical data, and advanced machine-learning methodologies will improve diagnostic, prognostic, and predictive accuracy in patients with pancreatic disease, and facilitate clinical decision and management towards precision medicine.