Artificial intelligence in population breast cancer screening: A two-year cost-effectiveness analysis
In brief
AI triage-only screening saves $4 per woman, misses 0.5 cancers per 1000
In an Australian model of 108,970 mammograms, using AI to skip human reads for low-risk cases reduced cost by about $4 per screened woman but detected roughly half a cancer fewer per 1000 screens, yielding an incremental cost of $8,200 per extra cancer found. Sensitivity tests showed this approach is unlikely to be cost-effective, and further real-world trials are needed.
- Journal
- Social science & medicine (1982) (Q1)
- Published
- 21 August 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Joanne Scarfe, M Luke Marinovich, Nehmat Houssami, Alison Pearce
- PMID
- 42664747
- DOI
- 10.1016/j.socscimed.2026.119704
Why clinicians should know about it
- Picked for Radiology, Radiation Oncology, Nuclear Medicine, Medical Physics and Imaging (top studies of the week, 30 August 2026): AI integration in breast cancer screening cost‑effectiveness
Abstract
OBJECTIVES: To investigate the cost-effectiveness of integrating artificial intelligence (AI) into breast cancer screening in Australia. METHODS: Standard screening practice (independent assessment of mammograms by two radiologists, with a third resolving discordance) was compared with three AI-human screen reading strategies: 1)Integrated (one radiologist and AI independently assess mammograms, with a second radiologist for discordance); 2)Triage Single (AI classifies mammograms as 'low-risk' or 'not low-risk' before human review, with standard reading for 'not low-risk' mammograms, single-radiologist reading for 'low-risk' mammograms); and 3)Triage Only (AI used to classify mammograms as 'low-risk' or 'not low-risk', standard reading for 'not low-risk' mammograms, no human reading for 'low-risk' mammograms). A two-year decision-analytic model was developed using a retrospective cohort of 108,970 mammograms from Australian women aged 50-74 (2015-2016). The model incorporated cancer detection and recall outcomes from an Australian health system perspective. Deterministic one-way and probabilistic sensitivity analyses assessed uncertainty. Scenario analyses incorporated cancer detection and recall estimates from a prospective randomized controlled trial to explore how base-case results may differ using an alternative screening workflow. RESULTS: In the base-case analysis, Triage Only was the most efficient strategy, saving an average of $4 per person screened but detecting 0.521 fewer cancers per 1000 people screened compared to standard practice (ICER $8206 per additional cancer detected). Integrated and Triage Single were dominated. Sensitivity analyses indicated Triage Only is unlikely to be cost-effective (cost-effective in <1% of simulations). Scenario analyses showed AI-supported screening was more effective, though more costly, than standard practice (ICER $1123 per additional cancer detected) and may be cost-effective. CONCLUSIONS: Our findings highlight that potential efficiency gains from AI may not necessarily translate into cost-effectiveness in breast cancer screening. Prospective evaluation incorporating real-world AI-assisted screening workflow and longer-term patient outcomes will be critical to determining the value of AI in population breast screening programs.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.