本文的目的是探讨人工智能（AI）应用于超声成像在妇科肿瘤学中的作用。检索了 Web of Science、PubMed 和 Scopus 数据库。所有研究均导入 RAYYAN QCRI 软件。使用 QUADAS-AI 工具评估纳入研究的整体质量。纳入 50 项研究，其中 37/50 (74.0%) 关于卵巢肿块或卵巢癌，5/50 (10.0%) 关于子宫内膜癌，5/50 (10.0%) 关于宫颈癌，3/50 (6.0%) 关于宫颈癌。 ）其他恶性肿瘤。大多数研究在受试者选择（即未指定样本量、来源或扫描仪型号；数据不是来自开源数据集；未进行成像预处理）和指标测试（未指定 AI 模型）方面存在很高的偏倚风险。外部验证）并且参考标准（即参考标准正确分类目标条件）和工作流程（即指标测试和参考标准之间的时间合理）的偏倚风险较低。大多数研究提出了用于卵巢肿块的诊断和组织病理学相关性的机器学习模型（33/50，66.0%），而其他研究则侧重于自动分割、放射组学特征的可重复性、图像质量的改善、治疗抵抗的预测、无进展生存期，以及基因突变。目前的证据支持人工智能作为补充临床和研究工具在妇科恶性肿瘤的诊断、患者分层和组织病理学相关性预测方面的作用。例如，人工智能模型区分良性和恶性卵巢肿块或预测其特定组织学的高性能可以提高成像方法的诊断准确性。© 2024 作者。约翰·威利出版的《国际癌症杂志》

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.