Ullah M, Akbar A, Yannarelli G. Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine. Artif Intell Cancer 2020; 1(2): 39-44 [DOI: 10.35713/aic.v1.i2.39]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Evidence Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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 Processing time: 64 Days and 9.8 Hours
Core Tip
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.
