Economic evaluation of artificial intelligence for breast cancer detection in the UK national breast screening programme
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
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
Harry Hill, Jim Chilcott, Andrew Metry
Harry Hill harry.hill@sheffield.ac.uk
Background:
Artificial intelligence is changing medical practices, speeding up radiological workflows, and indicating the long-term chance of disease. There are early signs from trials, where images are viewed at random by either AI or experienced radiologists, that AI powered image recognition for breast screening works better than the experienced radiologists. This will make sure that things like fatigue, overwork etc do not lead to cancers being missed. This would also lessen the workload for radiologists. However, there are worries that AI might identify extra diseases which could lead to overdiagnosis and overtreatment.
Aims and objectives:
The researchers will extend their existing SCHARR breast cancer screening model to work out the cost comparison of three AI-supported breast cancer screening strategies in the UK. These strategies include AI-only reading, one radiologist plus AI, and two radiologists plus AI, compared to the existing practice involving two radiologists.
Methods:
The model will consider lifetime health outcomes, NHS costs, and changes to NHS staffing requirements. The research will use data from a randomised controlled trial already being carried out in Sweden and other possible sources of evidence where AI has been used for cancer detection in a national breast screening service.
Policy relevance & dissemination:
The policy implications of this research are shown by how it agrees with the NHS's aims to use AI within clinical practice. For example, the UK National Screening Committee has called for an in-depth economic analysis to see how AI influences clinical outcomes and the broader health of the population. A report will be produced, and the findings will be reported at an international academic conference.
This study looked at whether artificial intelligence (AI) could be used to support breast cancer screening in the UK. Breast cancer screening saves lives by finding cancers early, often before symptoms appear. At the moment, every mammogram is checked by two human experts. This “double reading” reduces the risk of cancers being missed, but it involves a lot of staff time and is becoming harder to maintain because there are not enough radiologists to meet demand.
AI systems have been developed that can read mammograms and spot possible cancers. Research has already shown that AI can be about as accurate as human readers. But until now, no one had properly studied whether using AI would also make financial sense for the NHS, or if it could improve health outcomes if used in the national screening programme.
To answer this, the researchers built a computer model that copied how breast screening works in the NHS. They used data from a large trial in Sweden, where AI was tested in real screening clinics, and then adjusted it to match the UK’s system. The model compared three different approaches: the current system of two human readers, a system where one human reader works alongside AI, and a system with two human readers plus AI. They looked at how many cancers each approach would detect, how many deaths could be avoided, how long women lived after diagnosis, and the overall costs to the NHS.
The results were clear. The option of one human reader plus AI performed as well as the current system, and in some ways was even better. It found slightly more cancers, detected more at an earlier stage, and reduced the number of deaths. At the same time, it would save the NHS about £6 million each year because it cut down on staff costs and avoided some expensive treatments by catching cancers at an earlier stage. Using two humans plus AI gave the very best results in terms of cancers found and lives saved, but it was much more expensive, making it poor value for money. Letting AI work alone was the cheapest option but it led to more deaths and worse outcomes, so it would not be acceptable in practice.
The study suggests that replacing one of the two human readers with AI is the best balance for the NHS. It would save money, detect more cancers early, prevent deaths, and ease the strain on an overstretched workforce. It could also help reduce delays in getting screening results back to patients.
However, there are still some uncertainties. The cost of AI depends on the system used, and it is not yet known if AI always finds cancers at exactly the same stage as human readers. The study also did not consider issues such as how much patients would trust AI, whether it would affect attendance at screening, or how radiologists might use their time if freed up by this change. Over time, as AI systems improve, the benefits may grow even more.
Overall, this research provides strong evidence that combining AI with one human reader could be a safe, effective, and cost-saving improvement to the UK breast cancer screening programme. It could help the NHS save millions of pounds each year, improve survival for women with breast cancer, and relieve pressure on the workforce at a time when early detection is more important than ever.