Evidence map›Paper›PMID 40262029›Full record

ArticleCancer2025

Implementing artificial intelligence in breast cancer screening: Women's preferences.

Alison Pearce, Stacy Carter, Helen Ml Frazer, Nehmat Houssami, Mary Macheras-Magias, Genevieve Webb, M Luke Marinovich

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alison PearceThe Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-5690-9542
Stacy CarterAustralian Centre for Health Engagement, Evidence and Values, School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia.
Helen Ml FrazerSt Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia.
Nehmat HoussamiThe Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council New South Wales, Sydney, New South Wales, Australia.
Mary Macheras-MagiasSeat at the Table representative, Breast Cancer Network Australia, Camberwell, Victoria, Australia.
Genevieve WebbHealth Consumers New South Wales, Sydney, New South Wales, Australia.
M Luke MarinovichThe Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council New South Wales, Sydney, New South Wales, Australia.

Funding

National Breast Cancer Foundation 2023/IIRS0028National Breast Cancer Foundation EC-21-001National Health and Medical Research Council 1194410Sydney Cancer Institute
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) could improve accuracy and efficiency of breast cancer screening. However, many women distrust AI in health care, potentially jeopardizing breast cancer screening participation rates. The aim was to quantify community preferences for models of AI implementation within breast cancer screening.

methodsAn online discrete choice experiment survey of people eligible for breast cancer screening aged 40 to 74 years in Australia. Respondents answered 10 questions where they chose between two screening options created by an experimental design. Each screening option described the role of AI (supplementing current practice, replacing one radiologist, replacing both radiologists, or triaging), and the AI accuracy, ownership, representativeness, privacy, and waiting time. Analysis included conditional and latent class models, willingness-to-pay, and predicted screening uptake.

resultsThe 802 participants preferred screening where AI was more accurate, Australian owned, more representative and had shorter waiting time for results (all p < .001). There were strong preferences (p < .001) against AI alone or as triage. Three patterns of preferences emerged: positive about AI if accuracy improves (40% of sample), strongly against AI (42%), and concerned about AI (18%). Participants were willing to accept AI replacing one human reader if their results were available 10 days faster than current practice but would need results 21 days faster for AI as triage. Implementing AI inconsistent with community preferences could reduce participation by up to 22%.

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerPatient PreferenceAdultAgedAustraliaFemaleHumansMammographyMass ScreeningMiddle AgedSurveys and Questionnairesartificial intelligencebreast neoplasmsearly detection of cancerhealth services researchpatient preference

Identifiers

PMID40262029
PMCPMC12013981

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.