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Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme

Abstract

Background Researchers and developers are evaluating the use of mammogram readers that use artificial intelligence (AI) in clinical settings.

Objectives This study examines the attitudes of women, both current and future users of breast screening, towards the use of AI in mammogram reading.

Methods We used a cross-sectional, mixed methods study design with data from the survey responses and focus groups. We researched in four National Health Service hospitals in England. There we approached female workers over the age of 18 years and their immediate friends and family. We collected 4096 responses.

Results Through descriptive statistical analysis, we learnt that women of screening age (≥50 years) were less likely than women under screening age to use technology apps for healthcare advice (likelihood ratio=0.85, 95% CI 0.82 to 0.89, p<0.001). They were also less likely than women under screening age to agree that AI can have a positive effect on society (likelihood ratio=0.89, 95% CI 0.84 to 0.95, p<0.001). However, they were more likely to feel positive about AI used to read mammograms (likelihood ratio=1.09, 95% CI 1.02 to 1.17, p=0.009).

Discussion and Conclusions Women of screening age are ready to accept the use of AI in breast screening but are less likely to use other AI-based health applications. A large number of women are undecided, or had mixed views, about the use of AI generally and they remain to be convinced that it can be trusted.

  • health care
  • information science
  • public health

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information. Datasets are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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