ArticleHealth expectations : an international journal of public participation in health care and health policy2025
Developing a Typology of Women's Attitudes Towards AI Use in the BreastScreen Programme-A Qualitative Study With BreastScreen Victoria Clients.
Article in Health expectations : an international journal of public participation in health care and health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Patient Concerns Regarding Artificial Intelligence Applications in Health Care: Systematic Review and Meta-Synthesis Based on Social Ecological Theory.Journal of medical Internet research · 2026Pooled it
- Developing a Typology of Women's Attitudes Towards AI Use in the BreastScreen Programme-A Qualitative Study With BreastScreen Victoria Clients.Health expectations : an international journal of public participation in health care and health policy · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThere is growing scientific evidence supporting the potential of artificial intelligence (AI) to enhance breast cancer screening by improving the accuracy and efficiency of mammography interpretation. Aligned with this, several empirical studies, predominantly quantitative, have explored lay women's perceptions of AI in breast screening, often framing attitudes in binary terms-positive or negative. This approach can overlook the complexity and nuance of women's views.
aimThis article aims to unpack that complexity by developing a typology of women's attitudes towards the use of AI in the breast screening service. It builds on Birkland's (2019) information and communication technology (ICT) user typology among older adults and further explores the relationship between the attitude types and varying levels of AI acceptability.
methodAdopting an interpretative qualitative research approach, we conducted a combination of focus groups, paired interviews and one-on-one interviews with 26 women who had participated in the BreastScreen programme in Victoria, Australia. Data were thematically analysed using inductive coding.
findingsThe analysis identified four attitude types-Enthusiast, Practicalist, Traditionalist and Guardian. Each type reflected unique motivations and experiences that shaped each participant's acceptance and rejection of AI. Most participants were classified as either Enthusiasts or Practicalists, indicating a generally high or moderate level of AI acceptance. Enthusiasts viewed AI as an exciting and necessary progression, and Practicalists valued its practical utility as a useful tool. Both groups shared the belief that AI represents the future of healthcare, underpinned by technological advancement. Traditionalists, on the other hand, expressed a preference for the status quo, advocating for the exclusive role of human doctors. Guardians typically had higher levels of AI knowledge and advocated for a cautious approach, citing social and ethical concerns about AI integration.
conclusionThe typology illustrates that the BreastScreen Victoria clients' attitudes towards AI are more nuanced and dynamic than a simple positive-negative dichotomy. Recognising these perspectives is critical for designing AI implementation strategies that are sensitive to the needs and concerns of care recipients. PATIENT OR PUBLIC CONTRIBUTION: This study was shaped by extensive stakeholder engagement with BreastScreen Victoria and its consumer representatives from the outset. Research materials were collaboratively developed and reviewed, ensuring the study design was fit-for-purpose.
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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.