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Cost-effectiveness requirements for implementing artificial intelligence technology in the Women’s UK Breast Cancer Screening service

Author

Listed:
  • Armando Vargas-Palacios

    (University of Leeds
    Universidad Anáhuac)

  • Nisha Sharma

    (Leeds Teaching Hospital NHS Trust)

  • Gurdeep S. Sagoo

    (University of Leeds
    Newcastle University)

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.

Suggested Citation

  • Armando Vargas-Palacios & Nisha Sharma & Gurdeep S. Sagoo, 2023. "Cost-effectiveness requirements for implementing artificial intelligence technology in the Women’s UK Breast Cancer Screening service," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41754-0
    DOI: 10.1038/s41467-023-41754-0
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    References listed on IDEAS

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    1. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    2. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
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