Literature DB >> 33035503

Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions.

Ross W Filice1, John Mongan2, Marc D Kohli3.   

Abstract

Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should decide whether there is a need to independently verify performance or accept vendor-provided data. Successful implementations will consider who will receive AI results, how results will be presented, and the impact on efficiency. The article provides education on infrastructure considerations including the benefits and drawbacks of best-of-breed and platform approaches in addition to highly specialized server requirements like graphical processing unit availability. Finally, the article presents financial and quality and safety considerations, some of which are unique to AI. Examples include whether additional revenue could be obtained, as in the case of mammography, and whether an AI model unintentionally leads to reinforcing healthcare disparities.
Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; health care disparities; infrastructure; machine learning; purchasing; quality and safety

Mesh:

Year:  2020        PMID: 33035503     DOI: 10.1016/j.jacr.2020.09.045

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  2 in total

1.  How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

Authors:  Mohammad H Rezazade Mehrizi; Simon H Gerritsen; Wouter M de Klerk; Chantal Houtschild; Silke M H Dinnessen; Luna Zhao; Rik van Sommeren; Abby Zerfu
Journal:  Eur Radiol       Date:  2022-08-18       Impact factor: 7.034

Review 2.  Updates in Artificial Intelligence for Breast Imaging.

Authors:  Manisha Bahl
Journal:  Semin Roentgenol       Date:  2021-12-31       Impact factor: 0.709

  2 in total

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