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Review
. 2022 Oct;52(11):2087-2093.
doi: 10.1007/s00247-021-05114-8. Epub 2021 Jun 12.

How does artificial intelligence in radiology improve efficiency and health outcomes?

Kicky G van Leeuwen  1 , Maarten de Rooij  2 , Steven Schalekamp  2 , Bram van Ginneken  2 , Matthieu J C M Rutten  2   3
Affiliations
Review

How does artificial intelligence in radiology improve efficiency and health outcomes?

Kicky G van Leeuwen et al. Pediatr Radiol. 2022 Oct.

Abstract

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

Keywords: Artificial intelligence; Evidence-based practice; Impact analysis; Innovation; Pediatrics; Radiology; Value-based health care.

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Conflict of interest statement

None

Figures

Fig. 1
Fig. 1
Six objectives that can be pursued with artificial intelligence in radiology to improve efficiency and health outcomes
Fig. 2
Fig. 2
Number of artificial intelligence products in radiology brought to market based on data from [3]
See this image and copyright information in PMC

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