癌症是导致死亡的主要原因之一，因此及时诊断和预后非常重要。利用 AI（人工智能），医疗服务提供者能够以能够带来更好整体结果的方式组织和处理患者数据。这篇综述论文旨在探讨人工智能在诊断、预后和临床应用中的不同用途。利用 PubMed 和 EBSCO 数据库查找 2020 年 1 月 1 日至 2023 年 12 月 22 日期间的出版物。文章是使用“人工智能”和“机器学习”等关键搜索术语收集的。该集合中包括人工智能在使用多组学数据、放射组学、病理组学以及临床和实验室数据确定癌症诊断和预后方面的应用研究。由此产生的 89 项研究根据所使用的数据类型分为八个部分，然后进一步细分为分别侧重于癌症诊断和预后的两个小部分。八项研究整合了不止一种形式的组学，即基因组学、转录组学、表观基因组学和蛋白质组学。将人工智能与组学和临床数据一起纳入癌症诊断和预后代表着一项重大进步。鉴于人工智能在该领域的巨大潜力，持续的前瞻性研究对于增强算法的可解释性和确保安全的临床整合至关重要。

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.