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Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System

Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence... SUMMARY: Clinical adoption of an artificial intelligence–enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence–enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System

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References (59)

Publisher
American Journal of Neuroradiology
Copyright
© 2023 by American Journal of Neuroradiology
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.a7850
Publisher site
See Article on Publisher Site

Abstract

SUMMARY: Clinical adoption of an artificial intelligence–enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence–enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.

Journal

American Journal of Neuroradiology – American Journal of Neuroradiology

Published: May 1, 2023

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