Advertisement

Impact of the Artificial Nudge

      Rationale and Objectives

      Artificial intelligence (AI) is playing a growing role in the field of radiology. This article seeks to help readers quantify its impact when put into practice, using a lung nodule flagger as an example.

      Materials and Methods

      The one-time and ongoing costs associated with AI are explored. Costs are divided into three categories: direct costs, costs associated with operational changes, and downstream costs. Examples of each are provided.

      Results

      A framework for estimating the financial impact of AI is provided.

      Conclusion

      The impact of AI is quantifiable, but estimates of its financial impact may not be portable across contexts. Different organizations may implement AI in different ways due to differences in clinical practices. Furthermore, different organizations have different hurdle rates for their investments. Finally, international cost-effectiveness analyses may not be generalizable due to differences in both practice patterns and the valuation placed upon quality. When quantifying the impact of AI, organizations should consider relying upon pilots and data from other similarly-situated organizations.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      REFERENCES

        • Armato S.G.
        • Li F.
        • Giger M.L.
        • et al.
        Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.
        Radiology. 2002; 225: 685-692
        • Powell A.C.
        • Bowman M.B.
        • Harbin H.T.
        Reimbursement of apps for mental health: current practices and potential pathways.
        JMIR Ment Health. 2019;
        • Gray B.R.
        • Gunderman R.B.
        The McDonaldization of radiology.
        J Am Coll Radiol. 2018; 15: 689-691
        • Powell A.C.
        • Mirhadi A.J.
        • Loy B.A.
        • et al.
        Presentation at computed tomography (CT) scan of the thorax and first year diagnostic and treatment utilization among patients diagnosed with lung cancer.
        PLoS One. 2017; 12e0181319
        • Powell A.C.
        • Rogstad T.L.
        • Winchester D.E.
        • et al.
        Discordance in Clinical Recommendations regarding the Use of Imaging.
        Am J Med Qual. 2019; 21 (1062860619851561)
        • Shiroiwa T.
        • Sung Y.K.
        • Fukuda T.
        • et al.
        International survey on willingness‐to‐pay (WTP) for one additional QALY gained: what is the threshold of cost effectiveness?.
        Health Econ. 2010; 19: 422-437