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Artif Intell Med Imaging. Apr 28, 2022; 3(2): 33-41
Published online Apr 28, 2022. doi: 10.35711/aimi.v3.i2.33
Artificial intelligence: Advances and new frontiers in medical imaging
Marc R Fromherz, Mina S Makary
Marc R Fromherz, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
Mina S Makary, Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
Author contributions: All authors contributed to the writing and preparation of the manuscript.
Conflict-of-interest statement: The authors declare they have no conflicts of interest.
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: Mina S Makary, MD, Assistant Professor, Attending Doctor, Director, Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave 4th Floor Faculty Office Tower, Columbus, OH 43210, United States. mina.makary@osumc.edu
Received: December 20, 2021
Peer-review started: December 20, 2021
First decision: January 26, 2022
Revised: February 20, 2022
Accepted: April 21, 2022
Article in press: April 21, 2022
Published online: April 28, 2022
Abstract

Artificial intelligence (AI) has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago. These algorithms, ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks, have continuously sought to revolutionize medical imaging. With the number of imaging studies outpacing the amount of trained of readers, AI has been implemented to streamline workflow efficiency and provide quantitative, standardized interpretation. AI relies on massive amounts of data for its algorithms to function, and with the wide-spread adoption of Picture Archiving and Communication Systems (PACS), imaging data is accumulating rapidly. Current AI algorithms using machine-learning technology, or computer aided-detection, have been able to successfully pool this data for clinical use, although the scope of these algorithms remains narrow. Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage, however interpretation has generally remained a human responsibility for now. In this review article, we will summarize the current successes and limitations of AI in radiology, and explore the exciting prospects that deep-learning technology offers for the future.

Keywords: Artificial intelligence, Machine-learning, Deep-learning, Radiology workflow, Image interpretation

Core Tip: Artificial intelligence (AI) has been an increasingly publicized subject in the field of radiology. This review will attempt to summarize the evolving philosophy and mechanisms behind the AI movement as well as the current applications, limitations, and future directions of the field.