Evidence map›Paper›PMID 40446203›Full record

ArticlePloS one2025

How should artificial intelligence be used in breast screening? Women's reasoning about workflow options.

Diana Popic, M Luke Marinovich, Nehmat Houssami, Julie Hall, Stacy M Carter

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Diana PopicAustralian Centre for Health Engagement Evidence and Values, School of Social Sciences, Faculty of Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia.ORCID 0009-0009-7052-2799
M Luke MarinovichThe Daffodil Centre, The University of Sydney, Sydney, New South Wales, Australiaa Joint Venture with Cancer Council NSW, Sydney, New South Wales, Australia.ORCID 0000-0002-3801-8180
Nehmat HoussamiThe Daffodil Centre, The University of Sydney, Sydney, New South Wales, Australiaa Joint Venture with Cancer Council NSW, Sydney, New South Wales, Australia.ORCID 0000-0002-3641-952X
Julie HallAustralian Centre for Health Engagement Evidence and Values, School of Social Sciences, Faculty of Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia.
Stacy M CarterAustralian Centre for Health Engagement Evidence and Values, School of Social Sciences, Faculty of Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia.ORCID 0000-0003-2617-8694

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies show that breast screening participants are open to artificial intelligence (AI) in breast screening, but hold concerns about AI performance, governance, equitable access, and dependence on technology. Little is known of consumers' views on how AI should be used in breast screening practice. Our study aims to determine what matters most to women regarding AI use in the workflow of publicly funded breast screening programs, and how women choose between workflow options. We recruited forty women of screening age to learn about AI, the Australian breast screening program, and four possible workflows that include AI - one where AI works alone, and three different combinations of humans and AI. Participants then joined one of eight 90-minute dialogue groups to discuss their normative judgements on workflow options. Women proposed four conditions on AI deployment: preserving human control, evidence to assure AI performance, time to become familiar with AI, and clearly justifying the need for implementation. These informed women's unified rejection of AI working alone, and divided preferences across the other three workflows, as they traded off workflow attributes. Current evidence on AI performance convinced some women, but not others. Most women believed humans mitigate risk the best, so workflows should continue to be designed around them. Public breast screening services are trusted and valued by women, so significant changes require careful attention to outcomes relevant to women. Our results - women's detailed judgements on workflow design options - are new to the research literature. We conclude that women expect that AI only be deployed to do tasks it can do well, only where necessary, and only to fill gaps that radiologists cannot meet. Advancements in AI accuracy alone are unlikely to influence all women to accept AI making final decisions, if clinicians are available to perform the same task.

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerMass ScreeningWorkflowAdultAgedAustraliaFemaleHumansMammographyMiddle Aged

Identifiers

PMID40446203
PMCPMC12124851

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.