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Familiarity, confidence, and preference of Artificial Intelligence feedback and prompts by Australian breast cancer screening readers

Phuong Dung (Yun) Trieu , Melissa Barron, Zhengqiang Jiang, Seyedamir Tavakoli Taba, Ziba Gandomkar, Sarah Lewis

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: 65 radiologists, breast physicians and radiology trainees participated in an online survey which 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: 55% 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 mode (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 utilizing 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.

AH23275  Accepted 05 April 2024

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