Ramachandran A, Bhalla D, Rangarajan K, Pramanik R, Banerjee S, Arora C. Building and evaluating an artificial intelligence algorithm: A practical guide for practicing oncologists. Artif Intell Cancer 2022; 3(3): 42-53 [DOI: 10.35713/aic.v3.i3.42]
Corresponding Author of This Article
Krithika Rangarajan, MBBS, MD, Assistant Professor, Department of Radiology, All India Institute of Medical Sciences New Delhi, Ansari Nagar, New Delhi 110029, India. krithikarangarajan86@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Minireviews
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/
Anupama Ramachandran, Deeksha Bhalla, Department of Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
Krithika Rangarajan, Department of Radiology, All India Institute of Medical Sciences New Delhi, New Delhi 110029, India
Krithika Rangarajan, School of Information Technology, Indian Institute of Technology, Delhi 110016, India
Raja Pramanik, Department of Medical Oncology, Dr. B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi 110029, India
Subhashis Banerjee, Chetan Arora, Department of Computer Science, Indian Institute of Technology, Delhi 110016, India
Author contributions: Ramachandran A, Bhalla D, and Rangarajan K were involved in primary writing of the manuscript; Pramanik R gave inputs from a medical oncology perspective; Banerjee S and Arora C gave inputs from a computer science perspective.
Conflict-of-interest statement: All the authors have no conflict of interests to declare.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Krithika Rangarajan, MBBS, MD, Assistant Professor, Department of Radiology, All India Institute of Medical Sciences New Delhi, Ansari Nagar, New Delhi 110029, India. krithikarangarajan86@gmail.com
Received: February 14, 2022 Peer-review started: February 14, 2022 First decision: March 12, 2022 Revised: April 9, 2022 Accepted: July 13, 2022 Article in press: June 13, 2022 Published online: July 28, 2022 Processing time: 133 Days and 14.9 Hours
Abstract
The use of machine learning and deep learning has enabled many applications, previously thought of as being impossible. Among all medical fields, cancer care is arguably the most significantly impacted, with precision medicine now truly being a possibility. The effect of these technologies, loosely known as artificial intelligence, is particularly striking in fields involving images (such as radiology and pathology) and fields involving large amounts of data (such as genomics). Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients. Underst-anding these new claims and technologies requires a deep understanding of the field. In this review, we provide an overview of the basis of deep learning. We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects, as well as evaluate algorithms presented to them.
Core Tip: Designing projects and evaluating algorithms require a basic understanding of principles of machine learning. In addition, specific tasks have specific data requirements, annotation requirements, and applications. In this review, we describe the basic principles of machine learning, as well as explain various common tasks and their data requirements and applications in order to enable practicing oncologists to plan their own projects.
