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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 χ 2 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. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Objective. This study aimed to assess and compare the psychological wellbeing of Australian hospital clinical staff at three timepoints during the coronavirus disease 2019 (COVID-19) pandemic. Methods. An anonymous, online, cross-sectional survey was conducted at three timepoints during the COVID-19 pandemic (T1: May–June 2020; T2: October–December 2020; T3: November 2021–January 2022). The surveys were completed by nurses, midwives, doctors and allied health staff employed at a large metropolitan tertiary health service located in Melbourne, Australia. The Depression, Anxiety and Stress Scale (DASS-21) assessed respondents’ psychological wellbeing in the past week. General linear models were used to measure the effects of survey timepoint on DASS-21 subscale scores, adjusting for selected sociodemographic and health characteristics. Results. A total of 1470 hospital clinical staff completed at least one survey (T1: 668 (14.7%), T2: 358 (7.9%) and T3: 444 (9.8%)). Respondents’ sociodemographic characteristics were similar across the three timepoints and within professional discipline groups. Respondents’ psychological wellbeing was worse at T3 compared to the earlier survey timepoints. Adjusting for respondent characteristics, depression, anxiety and stress scores were significantly higher for respondents of the third survey compared to the first (P < 0.001). Conclusions. There was a significant and persistent negative impact on the psychological wellbeing of hospital clinical staff in Australia across waves of the COVID-19 pandemic. Hospital clinical staff would benefit from ongoing and continued wellbeing support during and after pandemic waves. [ABSTRACT FROM AUTHOR]