Chia Jolene Li Ling, He George Shiyao, Ngiam Kee Yuen, Hartman Mikael, Ng Qin Xiang, Goh Serene Si Ning
NUS Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr. S117597, Singapore 119077, Singapore.
Department of Surgery, National University Hospital, Singapore 119074, Singapore.
Cancers (Basel). 2025 Jan 9;17(2):197. doi: 10.3390/cancers17020197.
In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption.
In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included "Artificial Intelligence" and "Breast Cancer". Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes.
Finally, 84 articles were included. The majority were conducted in developed countries ( = 54). The majority of publications were in the last 10 years ( = 83). The six main themes for AI applications were AI for breast cancer screening ( = 32), AI for image detection of nodal status ( = 7), AI-assisted histopathology ( = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response ( = 23), AI in breast cancer margin assessment ( = 5), and AI as a clinical decision support tool ( = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes.
AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.
近年来，人工智能（AI）在推动全球乳腺癌护理方面展现出变革潜力。本综述旨在全面概述人工智能在乳腺癌护理中的应用，探讨其如何在全球范围内重塑诊断、治疗和管理，并讨论采用这些技术所带来的益处和挑战。
根据PRISMA - ScR及后续的综述指南，对PubMed、Web of Science、Cochrane图书馆和Embase从创刊至2024年5月底进行系统检索。关键词包括“人工智能”和“乳腺癌”。纳入基于人工智能在乳腺癌护理中应用的原始研究，并采用叙述性综合法进行数据提取和解读，研究结果归纳为连贯的主题。
最终纳入84篇文章。大多数研究在发达国家开展（n = 54）。大多数出版物发表于过去10年（n = 83）。人工智能应用的六个主要主题为乳腺癌筛查中的人工智能（n = 32）、淋巴结状态图像检测中的人工智能（n = 7）、人工智能辅助组织病理学（n = 8）、评估新辅助化疗（NACT）反应中的人工智能（n = 23）、乳腺癌切缘评估中的人工智能（n = 5）以及作为临床决策支持工具的人工智能（n = 9）。人工智能已被用作临床决策支持工具，以辅助乳腺癌治疗决策及多学科肿瘤委员会环境中的决策。总体而言，人工智能应用显示出更高的准确性和效率；然而，大多数文章未报告以患者为中心的临床结果。
人工智能在乳腺癌护理中的应用有望提高诊断准确性和治疗规划水平。然而，要推动该领域发展，必须解决人工智能应用中持续存在的挑战，如数据质量、算法透明度和资源差异等问题。
