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Choi: Concepts, Characteristics, and Clinical Validation of IBM Watson for Oncology
Review

Hanyang Medical Reviews 2017; 37(2): 49-60.

Published online: 30 November 2017

DOI: https://doi.org/10.7599/hmr.2017.37.2.49

Concepts, Characteristics, and Clinical Validation of IBM Watson for Oncology

Yoon Sup Choi1, 2, 3

1Digital Healthcare Institute, Seoul, Korea.

2Digital Healthcare Partners, Seoul, Korea.

3Department of Digital Health, Sungkyunkwan University, Seoul, Korea.

Corresponding Author: Yoon Sup Choi. Digital Healthcare Institute, Seocho-gu, Banpodero 30 gil, Room 1229, Seoul, Korea. yoonsup.choi@gmail.com

Received 27 July 2017       Revised 18 September 2017       Accepted 2 November 2017

© 2017 Hanyang University College of Medicine · Institute of Medical Science

Hanyang University College of Medicine · Institute of Medical Science

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The development of artificial intelligence revolutionizes many fields, and one of the representative fields is medical artificial intelligence. The world-renowned IBM Watson for Oncology (WFO) is a leading medical artificial intelligence that recommends treatment options to doctors based on medical records of cancer patients. This review examines the concepts and characteristics of WFOs and the challenges to be addressed. In particular, the accuracy and medical efficacy of WFO have not yet been fully validated, and it will be necessary to establish evidence through further clinical studies. Artificial intelligence will make fundamental changes in many areas of medicine in the future. It is necessary for the medical community to establish principles of how to maximize clinical benefits and minimize side effects.

Keywords: Artificial intelligence, Clinical trials, Evidence based medicine, Cancer, Machine learning

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