Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions.

Journal: Journal of the American College of Radiology : JACR
Published Date: Oct 6, 2020

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.

Authors

  • Ross W Filice
    MedStar Health, MedStar Georgetown University Hospital, 3800 Reservoir Rd, NW CG201, Washington DC, 20007 (R.W.F.); and MedStar Health, National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).
  • John Mongan
    From the Departments of Urology (T.C., M.U., H.C.C., M.S.) and Radiology and Biomedical Imaging (J.M., M.P.K., A.T., P.J., R.G., S.W.), University of California, San Francisco. 505 Parnassus Ave, M-391, San Francisco, CA 94143; and Division of Urology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, The Thai Red Cross Society, Bangkok, Thailand (M.U.).
  • Marc D Kohli
    Radiology and Biomedical Imaging, 505 Parnassus Ave, Moffit-391, San Francisco, CA, 94117, USA. marc.kohli@ucsf.edu.

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