Vargas-Palacios, A. orcid.org/0000-0002-6503-0134, Sharma, N. orcid.org/0000-0003-3991-0768 and Sagoo, G.S. orcid.org/0000-0003-1427-1437 (2023) Cost-effectiveness requirements for implementing artificial intelligence technology in the Women’s UK Breast Cancer Screening service. Nature Communications, 14. 6110. ISSN 2041-1723
Abstract
The UK NHS Women’s National Breast Screening programme aims to detect breast cancer early. The reference standard approach requires mammograms to be independently double-read by qualified radiology staff. If two readers disagree, arbitration by an independent reader is undertaken. Whilst this process maximises accuracy and minimises recall rates, the procedure is labour-intensive, adding pressure to a system currently facing a workforce crisis. Artificial intelligence technology offers an alternative to human readers. While artificial intelligence has been shown to be non-inferior versus human second readers, the minimum requirements needed (effectiveness, set-up costs, maintenance, etc) for such technology to be cost-effective in the NHS have not been evaluated. We developed a simulation model replicating NHS screening services to evaluate the potential value of the technology. Our results indicate that if non-inferiority is maintained, the use of artificial intelligence technology as a second reader is a viable and potentially cost-effective use of NHS resources.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Health Economics (Leeds) |
Funding Information: | Funder Grant number Kheiron Medical Technologies Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Oct 2023 15:15 |
Last Modified: | 04 Oct 2023 15:15 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41467-023-41754-0 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203915 |