William F Auffermann1, Elliott K Gozansky2, Srini Tridandapani3. 1. 1 Department of Radiology and Imaging Sciences, University of Utah Health, Salt Lake City, UT. 2. 2 Department of Radiology, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA. 3. 3 Department of Radiology, University of Alabama School of Medicine, 619 19th St S, JT N455E, Birmingham, AL 35249.
Abstract
OBJECTIVE: The goal of this article is to examine some of the current cardiothoracic radiology applications of artificial intelligence in general and deep learning in particular. CONCLUSION: Artificial intelligence has been used for the analysis of medical images for decades. Recent advances in computer algorithms and hardware, coupled with the availability of larger labeled datasets, have brought about rapid advances in this field. Many of the more notable recent advances have been in the artificial intelligence subfield of deep learning.
OBJECTIVE: The goal of this article is to examine some of the current cardiothoracic radiology applications of artificial intelligence in general and deep learning in particular. CONCLUSION: Artificial intelligence has been used for the analysis of medical images for decades. Recent advances in computer algorithms and hardware, coupled with the availability of larger labeled datasets, have brought about rapid advances in this field. Many of the more notable recent advances have been in the artificial intelligence subfield of deep learning.