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RESEARCH ARTICLE
Online Early

Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers

Phuong Dung (Yun) Trieu https://orcid.org/0000-0001-7021-6331 A * , Melissa L. Barron https://orcid.org/0000-0002-1143-5486 A , Zhengqiang Jiang https://orcid.org/0000-0002-5835-1984 A , Seyedamir Tavakoli Taba https://orcid.org/0000-0001-8759-0063 A , Ziba Gandomkar https://orcid.org/0000-0001-6480-3572 A and Sarah J. Lewis https://orcid.org/0000-0002-4791-9845 A B
+ Author Affiliations
- Author Affiliations

A Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, D18- Level 7 - Susan Wakil Health Building, Camperdown, NSW 2006, Australia.

B School of Health Sciences, Western Sydney University, University Drive, Campbelltown, Locked Bag 1797, Penrith, NSW 2751, Australia.

* Correspondence to: phuong.trieu@sydney.edu.au

Australian Health Review https://doi.org/10.1071/AH23275
Submitted: 13 September 2023  Accepted: 5 April 2024  Published: 2 May 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.

Abstract

Objectives

This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection.

Methods

Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants’ responses to questions were compared using Pearson’s χ2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons.

Results

Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were ‘Displaying suspicious areas on mammograms with the percentage of cancer possibility’ (67.8%) and ‘Automatic mammogram classification (normal, benign, cancer, uncertain)’ (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying ‘somewhat happy’ to ‘extremely happy’) over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001).

Conclusion

The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.

Keywords: artificial intelligence, breast cancer, clinical application, early detection, mammography, radiology, screening, survey.

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