Abstract Background Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications. Methods A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar. Results The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation. Conclusion Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.

中文翻译：

---

机器学习在乳腺手术中的现状和未来：系统回顾

摘要 背景机器学习是一组能够自动检测大量数据中的模式、提取信息并利用其在不确定条件下执行决策的模型和方法。机器学习的潜力是巨大的，乳腺外科医生必须努力了解最新的知识及其应用。 方法从成立到 2021 年 12 月，对 Embase、MEDLINE、Cochrane 数据库和 Google Scholar 进行了系统数据库搜索，对 EMBASE、MEDLINE、Cochrane 数据库中探索机器学习和/或人工智能在乳腺手术中的应用的原始文章进行了搜索和谷歌学术。 结果检索产生了 477 篇文章，其中 14 项研究纳入本次综述，涉及 73 847 名患者。确定了机器学习应用的四个主要领域：手术结果的预测建模；基于乳腺成像的背景；乳腺癌患者的筛查和分类；并作为检测的网络实用程序。机器学习在术前计划以及为癌症和美容手术提供信息方面具有明显的价值。在预测死亡率、发病率和生活质量结果的所有研究中，机器学习都优于传统的统计模型。机器学习模式和关联可以支持规划、解剖可视化和手术导航。 结论机器学习在改善乳腺手术结果和以患者为中心的护理方面展示了有前景的应用。然而，将人工智能应用到日常手术实践中仍然存在重要的局限性和伦理问题。