Future Directions in Artificial Intelligence

Babak Saboury; Michael Morris; Eliot Siegel

doi:10.1016/j.rcl.2021.07.008

Future Directions in Artificial Intelligence

Radiol Clin North Am. 2021 Nov;59(6):1085-1095. doi: 10.1016/j.rcl.2021.07.008.

Authors

Babak Saboury¹, Michael Morris², Eliot Siegel³

Affiliations

¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10 Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, MD 21250, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
² Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10 Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, MD 21250, USA; Division of Clinical Informatics, Networking Health, 331 Oak Manor Drive STE 201, Glen Burnie, MD 21061, USA.
³ Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 South Greene Street, Baltimore, MD 20201, USA; Department of Diagnostic Imaging, VA Maryland Healthcare System, 10 North Greene Street, Baltimore, MD 21201, USA. Electronic address: esiegel@som.umaryland.edu.

PMID: 34689876
DOI: 10.1016/j.rcl.2021.07.008

Abstract

No one knows what the paradigm shift of artificial intelligence will bring to medical imaging. In this article, we attempt to predict how artificial intelligence will impact radiology based on a critical review of current innovations. The best way to predict the future is to anticipate, prepare, and create it. We anticipate that radiology will need to enhance current infrastructure, collaborate with others, learn the challenges and pitfalls of the technology, and maintain a healthy skepticism about artificial intelligence while embracing its potential to allow us to become more productive, accurate, secure, and impactful in the care of our patients.

Keywords: Artificial intelligence; Autonomous AI; Future of AI; Radiology information architecture; Radiophenomics.

Publication types

Review

MeSH terms

Artificial Intelligence / trends*
Diagnostic Imaging / methods*
Diagnostic Imaging / trends*
Humans
Image Interpretation, Computer-Assisted / methods*
Radiology / methods*
Radiology / trends*