ArticleNature communications2023
Cost-effectiveness requirements for implementing artificial intelligence technology in the Women's UK Breast Cancer Screening service.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Health Economic Evaluations of Breast Cancer Screening Strategies: A Systematic Review Focusing on Methodology and Quality Assessment.Applied health economics and health policy · 2026Pooled it
- Cost-effectiveness of artificial intelligence tools in radiology: a systematic review.European radiology · 2026Pooled it
- Economic evaluation of artificial intelligence for cancer detection in the UK breast screening programme.British journal of cancer · 2026Article
- Acceptability of digital health solutions and AI: qualitative interviews with perimenopausal women experiencing health inequalities.BMJ innovations · 2026Article
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.PLOS digital health · 2026Article
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Cost-effectiveness of two major population-based breast cancer screening programmes in China: a model-based economic evaluation.BMJ public health · 2026Article
- European Code Against Cancer, 5th edition - organised cancer screening programmes.Molecular oncology · 2026Review
- Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography.European radiology · 2026Article
- Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study.JMIR formative research · 2025Article
- Balancing Surgical Innovation and Risk: A Narrative Review of Emerging Technologies, Regulation, and Global Access.Cureus · 2025Review
- Artificial Intelligence in Senology - Where Do We Stand and What Are the Future Horizons?European journal of breast health · 2024Article
- Cost-effectiveness requirements for implementing artificial intelligence technology in the Women's UK Breast Cancer Screening service.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.