Trial reportThe patient2025
Preferences for the Use of Artificial Intelligence for Breast Cancer Screening in Australia: A Discrete Choice Experiment.
Trial report in The patient, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Preferences for AI-Enabled Health Care Technologies: Systematic Review of Discrete Choice Experiments and Reporting Quality Assessment Using the DIRECT Checklist.Journal of medical Internet research · 2026Pooled it
- Public Preferences for Emerging Cancer Screening Modalities: A Systematic Review of Discrete Choice Experiments.The patient · 2026Pooled it
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- Patient and Public Perceptions of Artificial Intelligence in Breast Imaging and Clinical Decision-Making: An Exploratory Cross-Sectional Survey Study.Diagnostics (Basel, Switzerland) · 2026Article
- Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.Journal of medical Internet research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundBreast cancer screening is considered an effective early detection strategy. Artificial intelligence (AI) may both offer benefits and create risks for breast screening programmes. To use AI in health screening services, the views and expectations of consumers are critical. This study examined the preferences of Australian women regarding AI use in breast cancer screening and the impact of information on preferences using discrete choice experiments.
methodsThe experiment presented two alternative screening services based on seven attributes (reading method, screening sensitivity, screening specificity, time between screening and receiving results, supporting evidence, fair representation, and who should be held accountable) to 2063 women aged between 40 and 74 years recruited from an online panel. Participants were randomised into two arms. Both received standard information on AI use in breast screening, but one arm received additional information on its potential benefits. Preferences for hypothetical breast cancer screening services were modelled using a random parameter logit model. Relative attribute importance and uptake rates were estimated.
resultsParticipants preferred mixed reading (radiologist + AI system) over the other two reading methods. They showed a strong preference for fewer missed cases with a high attribute relative importance. Fewer false positives and a shorter waiting time for results were also preferred. Strength of preferences for mixed reading was significantly higher compared to two radiologists when additional information on AI is provided, highlighting the impact of information.
conclusionsThis study revealed the preferences among Australian women for the use of AI-driven breast cancer screening services. Results generally suggest women are open to their mammograms being read by both a radiologist and an AI-based system under certain conditions.
Indexed as
Identifiers
What OpenQuestion holds
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.