尽管人工智能（AI）在增强乳腺癌诊断方面展示了潜力，但AI算法在临床实践中的应用面临各种障碍。本次范围性综述旨在确定这些障碍和促进因素，以突出发展和应用乳腺癌成像中的AI解决方案的关键考虑因素。在PubMed、Web of Science、CINHAL、Embase、IEEE和ArXiv等六个数据库中进行了2012年至2022年的文献搜索。如果有关乳腺临床成像中AI的构思或实施中描述了一些障碍和/或促进因素，这些文章被纳入研究。我们排除了仅关注性能的研究，或者使用非临床放射技术设置获得的数据并且未涉及真实患者的研究。共纳入了107篇文章。我们确定了与数据（B1）、黑箱与信任（B2）、算法与构思（B3）、评估与验证（B4）、法律、伦理和经济问题（B5）以及教育（B6）相关的六个主要障碍，以及与数据（F1）、临床影响（F2）、算法与构思（F3）、评估与验证（F4）和教育（F5）相关的五个主要促进因素。该范围性综述突出了在临床实践中精心设计、部署和评估AI解决方案的需求，涉及所有利益相关者，以实现医疗保健的改进。通过提出解决方案，确定障碍和促进因素，可以指导和告知未来的研究和利益相关者，以改进乳腺癌检测的AI设计和实施。- 六个主要的障碍包括数据、黑箱和信任、算法和构思、评估和验证、法律、伦理和经济问题以及教育。- 五个主要的促进因素包括数据、临床影响、算法和构思、评估和验证以及教育。- 需要协调所有利益相关者的实施来改善乳腺癌的诊断与AI。© 2023. 作者们。

Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging.A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients.A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5).This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare.The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice.• Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.© 2023. The Author(s).