Economic evaluation of artificial intelligence for breast cancer detection in the UK national breast screening programme
Theme 4: Applied Economic Evaluation
There is a global shortage of breast radiologists (1) and in the UK, there is currently a 29% deficit in clinical radiologists (2). This shortfall is projected to increase to 40% within the next five years(2). One potential solution to address these challenges is the application of artificial intelligence (AI) (3). Several retrospective studies have indicated that AI can achieve diagnostic accuracy comparable to that of human radiologists when assessing breast screening mammograms(3). While these studies have yielded promising results, they are susceptible to biases due to their retrospective designs and do not evaluate how AI integrates into existing screening workflows. Recently, the initial findings from the first population-based randomised trial of AI-supported breast screening were published (4). This trial recruited 58,344 Swedish women aged 40–74 years, and compared three AI-supported mammography approaches (AI-only reading, one radiologist plus AI, two radiologists plus AI) with standard practice of two radiologists reading the mammogram screen. All AI alternatives detected at least as many cancers as standard practice, and the AI alternatives with fewer radiologists resulted in a significantly reduced workload for screen reading. Additionally, the approach involving two radiologists plus AI detected more cancers over the one year trial period (269 vs. 250) while maintaining a similar workload. Although this RCT provides encouraging results, the RCT design does not assess the integration of AI system into the UK screening programme, consider the effect of AI screening on the entire population of women eligible for screening, and consider its impact on important non-clinical outcomes such net population health (QALYs), NHS resources and workforce capacity.
1. Nightingale, J., Sevens, T., Appleyard, R., Campbell, S. and Burton, M., 2023. Retention of radiographers in the NHS: Influencing factors across the career trajectory. Radiography, 29(1), pp.76-83.
2. Royal college of Radiologists. Clinical Radiology Workforce Census.Aviliable at: https://www.rcr.ac.uk/sites/default/files/documents/rcr_clinical_radiology_workforce_census_2023.pdf
3. van Nijnatten, T.J.A., Payne, N.R., Hickman, S.E., Ashrafian, H. and Gilbert, F.J., 2023. Overview of trials on artificial intelligence algorithms in breast cancer screening–A roadmap for international evaluation and implementation. European Journal of Radiology, 167, p.111087.
4. Lång K, Josefsson V, Larsson A-M, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 2023; 24: 936–44
Aims
This project would extend our existing modelling to assess the workforce capacity impact and cost-effectiveness of three alternative approaches for introducing AI-supported screen-reading into the UK national breast screening programme
Project Team
Harry Hill, Jim Chilcott, Andrew Metry
Contact
Harry Hill harry.hill@sheffield.ac.uk