Seth Ishith, Lim Bryan, Joseph Konrad, Gracias Dylan, Xie Yi, Ross Richard J, Rozen Warren M
Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia.
Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia.
Gland Surg. 2024 Mar 27;13(3):395-411. doi: 10.21037/gs-23-414. Epub 2024 Mar 22.
We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application.
Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded.
AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence.
Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
我们见证了人工智能（AI）技术的巨大进步。乳腺外科作为普通外科的一个亚专业，显著受益于人工智能技术。本综述旨在评估人工智能如何融入乳腺外科实践，评估其在改善手术效果和提高运营效率方面的有效性，并确定未来研究和应用的潜在领域。
两位作者独立对1950年1月1日至2023年9月4日期间的PubMed、谷歌学术、EMBASE和Cochrane CENTRAL数据库进行了全面检索，使用与人工智能相关的关键词，并结合乳腺外科或癌症。检索重点为英文出版物，通过对标题、摘要和全文的细致筛选确定相关性，随后对这些文章中的参考文献进行额外审查。该综述涵盖了一系列说明人工智能在乳腺外科应用的研究，包括从病变诊断到术后随访。专门关注乳房重建的出版物被排除。
人工智能模型在乳腺外科领域具有术前、术中和术后应用。利用乳腺成像扫描和患者数据，设计了人工智能模型来预测乳腺癌风险并确定乳腺癌手术的必要性。此外，利用乳腺成像扫描和组织病理学切片，模型用于检测、分类、分割、分级和分期乳腺肿瘤。术前应用包括患者教育和预期美学效果展示。还设计了模型为精确肿瘤切除和切缘状态评估提供术中辅助。此外，人工智能被用于预测术后并发症、生存率和癌症复发。
需要进行更多研究，将人工智能模型从实验阶段推进到医疗保健中的实际应用。随着人工智能的快速发展，预计未来几年会有更多应用，包括直接进行乳腺手术。乳腺外科医生应了解人工智能在乳腺外科应用的进展，以便为患者提供最佳护理。
