The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

中文翻译：

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人工智能在妇科癌症数字病理学中的作用

癌症的诊断通常基于组织病理学切片或载玻片上的活检。随着肿瘤学数据的快速增长，人工智能（AI）方法极大地增强了我们从数字组织病理学图像中提取定量信息的能力。妇科癌症是影响全世界妇女健康的主要疾病。它们的特点是死亡率高、预后差，这凸显了早期检测、治疗和识别预后因素的至关重要性。这篇综述重点介绍了人工智能在妇科癌症中使用数字化组织病理学幻灯片的各种临床应用。特别是，深度学习模型在准确诊断、组织病理学亚型分类以及预测治疗反应和预后方面显示出了希望。此外，与转录组学、蛋白质组学和其他多组学技术的整合可以为疾病的分子特征提供有价值的见解。尽管人工智能潜力巨大，但仍然存在巨大挑战。需要进一步改进数据采集和模型优化，并探索更广泛的临床应用，例如生物标志物的发现。