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
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REFERENCES
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Article info
Publication history
Accepted:
September 8,
2019
Received:
August 1,
2019
Identification
Copyright
© 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
