作为数据驱动的科学，人工智能 (AI) 为不断发展的卫生系统铺平了一条充满希望的道路，其中充满了精准肿瘤学的激动人心的机会。尽管肿瘤人工智能在肺癌、乳腺肿瘤和脑恶性肿瘤等领域取得了巨大成功，但人们很少关注人工智能对妇科肿瘤学的影响。特此，本综述揭示了最先进的人工智能技术对妇科肿瘤（特别是宫颈癌、卵巢癌和子宫内膜癌）患者的精细化风险分层和全程管理所做出的日益增长的贡献，重点是从临床数据（电子健康记录）、癌症成像（包括放射成像、阴道镜图像、细胞学和组织病理学数字图像）以及分子分析（基因组学、转录组学、代谢组学等）中提取的信息和特征。然而，除了性能验证之外，仍然存在值得注意的挑战。因此，这项工作进一步描述了人工智能模型在实际实施中面临的限制和挑战，以及解决这些问题的潜在解决方案。版权所有 © 2023。由 Elsevier Ltd 出版。

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.Copyright © 2023. Published by Elsevier Ltd.