©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Transl Med. Mar 15, 2021; 9(1): 1-10
Published online Mar 15, 2021. doi: 10.5528/wjtm.v9.i1.1
Published online Mar 15, 2021. doi: 10.5528/wjtm.v9.i1.1
Machine intelligence for precision oncology
Nelson S Yee, Department of Medicine, The Pennsylvania State University College of Medicine, Penn State Cancer Institute, Penn State Health Milton S. Hershey Medical Center, Hershey, PA 17033-0850, United States
Author contributions: Yee NS performed research and wrote the paper.
Conflict-of-interest statement: Dr. Yee reports grants from Ipsen Biopharmaceuticals, other from Caris Life Sciences, other from Novartis, outside the submitted work.
Corresponding author: Nelson S Yee, BPharm, FACP, MD, PhD, Associate Professor, Attending Doctor, Department of Medicine, The Pennsylvania State University College of Medicine, Penn State Cancer Institute, Penn State Health Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033-0850, United States. nyee@pennstatehealth.psu.edu
Received: October 19, 2020
Peer-review started: October 19, 2020
First decision: November 16, 2020
Revised: December 22, 2020
Accepted: March 1, 2021
Article in press: March 1, 2021
Published online: March 15, 2021
Processing time: 139 Days and 16.8 Hours
Peer-review started: October 19, 2020
First decision: November 16, 2020
Revised: December 22, 2020
Accepted: March 1, 2021
Article in press: March 1, 2021
Published online: March 15, 2021
Processing time: 139 Days and 16.8 Hours
Core Tip
Core Tip: Artificial intelligence represents the future of healthcare particularly precision oncology for prevention, detection, risk assessment, and treatment of cancer. Application of machine learning- and deep learning-based algorithms in translational research has been demonstrated to improve accuracy of cancer diagnosis and anti-cancer drug development. Multi-disciplinary collaboration with resolution of ethical and regulatory issues of multi-modal machine intelligence are indicated for implementation of computer-assisted clinical decision on individualized patient management.