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As breast screening services move towards use of healthcare AI (HCAI) for screen reading, research on public views of HCAI can inform more person-centered implementation. We synthesise reviews of public views of HCAI in general, and review primary studies of women's views of AI in breast screening. People generally appear open to HCAI and its potential benefits, despite a wide range of concerns; similarly, women are open towards AI in breast screening because of the potential benefits, but are concerned about a wide range of risks. Women want radiologists to remain central; oversight, evaluation and performance, care, equity and bias, transparency, and accountability are key issues; women may be less tolerant of AI error than of human error. Using our recent Australian primary study, we illustrate both the value of informing participants before collecting data, and women's views. The 40 screening-age women in this study stipulated four main conditions on breast screening AI implementation: 1) maintaining human control; 2) strong evidence of performance; 3) supporting familiarisation with AI; and 4) providing adequate reasons for introducing AI. Three solutions were offered to support familiarisation: transparency and information; slow and staged implementation; and allowing women to opt-out of AI reading. We provide recommendations to guide both implementation of AI in healthcare and research on public views of HCAI. Breast screening services should be transparent about AI use and share information about breast screening AI with women. Implementation should be slow and staged, providing opt-out options if possible. Screening services should demonstrate strong governance to maintain clinician control, demonstrate excellent AI system performance, assure data protection and bias mitigation, and give good reasons to justify implementation. When these measures are put in place, women are more likely to see HCAI use in breast screening as legitimate and acceptable., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better understand the consequences of implementing this technology., Methods: Electronic databases were searched for external validation studies of the accuracy of AI algorithms in real-world screening mammograms. Descriptive synthesis was performed on error types and frequency. False negative proportions (FNP) and false positive proportions (FPP) were pooled within AI positivity thresholds using random-effects meta-analysis., Results: Seven retrospective studies (447,676 examinations; published 2019-2022) met inclusion criteria. Five studies reported AI error as false negatives or false positives. Pooled FPP decreased incrementally with increasing positivity threshold (71.83% [95% CI 69.67, 73.90] at Transpara 3 to 10.77% [95% CI 8.34, 13.79] at Transpara 9). Pooled FNP increased incrementally from 0.02% [95% CI 0.01, 0.03] (Transpara 3) to 0.12% [95% CI 0.06, 0.26] (Transpara 9), consistent with a trade-off with FPP. Heterogeneity within thresholds reflected algorithm version and completeness of the reference standard. Other forms of AI error were reported rarely (location error and technical error in one study each)., Conclusion: AI errors are largely interpreted in the framework of test accuracy. FP and FN errors show expected variability not only by positivity threshold, but also by algorithm version and study quality. Reporting of other forms of AI errors is sparse, despite their potential implications for adoption of the technology. Considering broader types of AI error would add nuance to reporting that can inform inferences about AI's utility., (© 2024. The Author(s).)

Competing Interests: NH receives funding via a National Breast Cancer Foundation Chair in Breast Cancer Prevention grant (EC-21-001) and National Health and Medical Research Council Investigator (Leader) grant (1194410). MLM receives funding via a National Breast Cancer Foundation Investigator Initiated Research Scheme grant (IIRS-20-011).

Purpose: The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography., Methods: A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool., Results: After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage., Conclusions: To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns., (Copyright © 2021 American College of Radiology. Published by Elsevier Inc. All rights reserved.)