ArticleBMJ open2024
Women's perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04778670 (Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD), which is not on this map. Cited by 19 papers, 3 of them syntheses 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.
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.
Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.Health technology assessment (Winchester, England) · 2026Pooled 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
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.European radiology · 2026Trial
- Preferences for the Use of Artificial Intelligence for Breast Cancer Screening in Australia: A Discrete Choice Experiment.The patient · 2025Trial
- Perspectives of Patients Regarding Artificial Intelligence and Its Application in Healthcare: A Qualitative Study.Journal of clinical nursing · 2026Article
- Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.European radiology · 2026Article
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.Diagnostics (Basel, Switzerland) · 2026Review
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Me, my doctor and AI: a qualitative interview study on patients' requirements for trustworthy use of AI-CDSS in ophthalmology.BMJ digital health & AI · 2026Article
- Accessing AI mammography reports impacts patient follow-up behaviors: the unintended consequences of including AI in patient portals.NPJ digital medicine · 2025Article
- 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
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- Risk inventory and mitigation actions for AI in medical imaging-a qualitative study of implementing standalone AI for screening mammography.BMC health services research · 2025Article
- Article
- Exploring trust factors in AI-healthcare integration: a rapid review.Frontiers in artificial intelligence · 2025Review
- Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review.Digital healthReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding women's perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women's perceptions and attitudes towards the use of AI in mammography.
methodSemistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis.
resultsIn general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice.
conclusionThe findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.
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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.