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. 2023 Jun 23;6(1):117.
doi: 10.1038/s41746-023-00861-4.

To pay or not to pay for artificial intelligence applications in radiology

Franziska Lobig  1 , Dhinagar Subramanian  2 , Michael Blankenburg  3 , Ankur Sharma  3 , Archana Variyar  2 , Oisin Butler  3
Affiliations

To pay or not to pay for artificial intelligence applications in radiology

Franziska Lobig et al. NPJ Digit Med. .

Abstract

Artificial Intelligence-supported digital applications (AI applications) are expected to transform radiology. However, providers need the motivation and incentives to adopt these technologies. For some radiology AI applications, the benefits of the application itself may sufficiently serve as the incentive. For others, payers may have to consider reimbursing the AI application separate from the cost of the underlying imaging studies. In such circumstances, it is important for payers to develop a clear set of criteria to decide which AI applications should be paid for separately. In this article, we propose a framework to help serve as a guide for payers aiming to establish such criteria and for technology vendors developing radiology AI applications. As a rule of thumb, we propose that radiology AI applications with a clinical utility must be reimbursed separately provided they have supporting evidence that the improved diagnostic performance leads to improved outcomes from a societal standpoint, or if such improved outcomes can reasonably be anticipated based on the clinical utility offered.

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Conflict of interest statement

Funding for development of the manuscript came from Bayer AG. Franziska Lobig, Michael Blankenburg, Oisin Butler and Ankur Sharma are employees of Bayer AG. Dhinagar Subramanian and Archana Variyar are employees of Qlaar LTD, a consulting firm which has received consulting fees from Bayer AG.

Figures

Fig. 1
Fig. 1
Proposed framework to determine whether to pay for radiology AI applications separately or additionally to underlying imaging studies.
See this image and copyright information in PMC

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