Evidence Review
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Cancer. Aug 28, 2020; 1(2): 39-44
Published online Aug 28, 2020. doi: 10.35713/aic.v1.i2.39
Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine
Mujib Ullah, Asma Akbar, Gustavo Yannarelli
Mujib Ullah, Asma Akbar, Institute for Immunity, Transplantation, Stem Cell Biology and Regenerative Medicine, School of Medicine, Stanford University, Palo Alto, CA 94304, United States
Mujib Ullah, Asma Akbar, Molecular Medicine, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304, United States
Gustavo Yannarelli, Laboratorio de Regulación Génica y Células Madre, Instituto de Medicina Traslacional, Trasplante y Bioingeniería, Universidad Favaloro-CONICET, Buenos Aires 1078, Argentina
Author contributions: All authors have made substantial contributions to conception, study design, analysis and interpretation of data; engaged in preparing the article or revising it analytically for essential intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.
Conflict-of-interest statement: The authors declare no conflict of interest regarding this article.
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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Mujib Ullah, MD, PhD, Assistant Professor, Senior Scientist, Institute for Immunity, Transplantation, Stem Cell Biology and Regenerative Medicine, School of Medicine, Stanford University, 3145 Porter Dr, Palo Alto, CA 94304, United States. ullah@stanford.edu
Received: July 6, 2020
Peer-review started: July 6, 2020
First decision: August 8, 2020
Revised: August 24, 2020
Accepted: August 27, 2020
Article in press: August 27, 2020
Published online: August 28, 2020

Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to convert raw input data into decision-making actions, like humans. AI programs are designed to make decisions, often using deep learning and computer-guided programs that analyze and process raw data into clinical decision making for effective treatment. New techniques for predicting cancer at an early stage are needed as conventional methods have poor accuracy and are not applicable to personalized medicine. AI has the potential to use smart, intelligent computer systems for image interpretation and early diagnosis of cancer. AI has been changing almost all the areas of the medical field by integrating with new emerging technologies. AI has revolutionized the entire health care system through innovative digital diagnostics with greater precision and accuracy. AI is capable of detecting cancer at an early stage with accurate diagnosis and improved survival outcomes. AI is an innovative technology of the future that can be used for early prediction, diagnosis and treatment of cancer.

Keywords: Artificial intelligence, Cancer, Clinical tumor prediction, Early detection of cancer, Clinical diagnosis, Personalized medicine

Core Tip: Early detection of cancer potentially enhances the chances for successful treatment and patient survival outcome. Artificial intelligence (AI), a field of computer science, aims to develop algorithms or computer programs with advanced analytical or predictive capabilities. The development of highly accurate AI algorithms for the early recognition of the disease is crucial not only for the rapid identification and diagnosis of cancer patients, but also for the treatment. Many AI platforms are being developed and approved by the US Food and Drug Administration for use in some areas of cancer, such as identifying suspicious lesions in cancer and interpretation of magnetic resonance imaging or computed tomography. Similarly, the Big Data to Knowledge initiative was launched by National Institute of Health to support the research and development of tools to integrate big data and data science into biomedical research. AI-guided clinical care has the potential to play an essential role in the screening, diagnosis and treatment of cancer.