Background: The advent of artificial intelligence (AI) has revolutionised many fields in healthcare. More recently, it has garnered interest in terms of its potential applications in histopathology, where algorithms are increasingly being explored as adjunct technologies that can support pathologists in diagnosis, molecular typing and prognostication. While many research endeavours have focused on solid tumours, gynaecological malignancies have nevertheless been relatively overlooked. The aim of this review was therefore to provide a summary of the status quo in the field of AI in gynaecological pathology by encompassing malignancies throughout the entirety of the female reproductive tract rather than focusing on individual cancers.Methods: This narrative/scoping review explores the potential application of AI in whole slide image analysis in gynaecological histopathology, drawing on both findings from the research setting (where such technologies largely remain confined), and highlights any findings and/or applications identified and developed in other cancers that could be translated to this arena.Results: A particular focus is given to ovarian, endometrial, cervical and vulval/vaginal tumours. This review discusses different algorithms, their performance and potential applications.Conclusions: The effective application of AI tools is only possible through multidisciplinary co-operation and training.
背景：人工智能的出现已彻底变革了医疗保健领域的诸多方面。近年来，其在组织病理学中的潜在应用日益受到关注，相关算法正越来越多地被探索作为辅助技术，以支持病理学家进行诊断、分子分型和预后评估。尽管许多研究集中于实体肿瘤，但妇科恶性肿瘤领域相对受到忽视。因此，本综述旨在通过涵盖整个女性生殖道的恶性肿瘤，而非聚焦于单一癌症，总结人工智能在妇科病理学领域的现状。 方法：本叙述性/范围性综述探讨了人工智能在妇科组织病理学全玻片图像分析中的潜在应用，借鉴了研究环境中的发现（目前此类技术主要仍局限于该领域），并重点介绍了在其他癌症中已识别和开发、可转化应用于此领域的研究成果和/或应用。 结果：本综述特别关注卵巢、子宫内膜、宫颈及外阴/阴道肿瘤，讨论了不同算法的性能及其潜在应用。 结论：人工智能工具的有效应用唯有通过多学科合作与培训方能实现。
An Overview of Artificial Intelligence in Gynaecological Pathology Diagnostics
肿瘤(癌症)患者之家