Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging.

Autor:	Bajaj S; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Khunte M; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Moily NS; Visage Imaging, San Diego, California., Payabvash S; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut., Wintermark M; Chair, Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas., Gandhi D; Director, Interventional Neuroradiology, University of Maryland School of Medicine, Baltimore, Maryland., Malhotra A; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut. Electronic address: ajay.malhotra@yale.edu.
Jazyk:	angličtina
Zdroj:	Journal of the American College of Radiology : JACR [J Am Coll Radiol] 2023 Dec; Vol. 20 (12), pp. 1241-1249. Date of Electronic Publication: 2023 Aug 11.
DOI:	10.1016/j.jacr.2023.06.034
Abstrakt:	Purpose: The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algorithms into clinical practice depends largely on whether this technology provides value in the clinical setting. The objective of this study was to analyze trends in FDA-cleared AI algorithms for neuroimaging and understand their value proposition as advertised by the AI developers and vendors. Methods: A list of AI algorithms cleared by the FDA for neuroimaging between May 2008 and August 2022 was extracted from the ACR Data Science Institute AI Central database. Product information for each device was collected from the database. For each device, information on the advertised value as presented on the developer's website was collected. Results: A total of 59 AI neuroimaging algorithms were cleared by the FDA between May 2008 and August 2022. Most of these algorithms (24 of 59) were compatible with noncontrast CT, 21 with MRI, 9 with CT perfusion, 8 with CT angiography, 3 with MR perfusion, and 2 with PET. Six algorithms were compatible with multiple imaging techniques. Of the 59 algorithms, websites were located that discussed the product value for 55 algorithms. The most widely advertised value proposition was improved quality of care (38 of 55 [69.1%]). A total of 24 algorithms (43.6%) proposed saving user time, 9 (15.7%) advertised decreased costs, and 6 (10.9%) described increased revenue. Product websites for 26 algorithms (43.6%) showed user testimonials advertising the value of the technology. Conclusions: The results of this study indicate a wide range of value propositions advertised by developers and vendors of AI algorithms for neuroimaging. Most vendors advertised that their products would improve patient care. Further research is necessary to determine whether the value claimed by developers is actually demonstrated in clinical practice. (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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