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.
癌症是主要致死疾病之一，这使得及时诊断与预后评估至关重要。人工智能的应用使医疗工作者能够以优化整体疗效的方式整合处理患者数据。本综述旨在探讨人工智能在癌症诊断、预后评估及临床应用中的多样化实践。研究通过PubMed和EBSCO数据库检索2020年1月1日至2023年12月22日期间发表的文献，采用"人工智能"与"机器学习"等关键词进行筛选。最终纳入89项研究，涵盖人工智能在多组学数据、放射组学、病理组学及临床实验室数据在癌症诊断与预后评估中的应用。根据数据类型将研究划分为八个类别，并进一步细分为诊断与预后两个子类，其中八项研究整合了基因组学、转录组学、表观基因组学与蛋白质组学等多组学数据。人工智能与多组学及临床数据的融合应用标志着该领域的重大进展。鉴于人工智能在此领域的巨大潜力，持续开展前瞻性研究对提升算法可解释性、确保临床安全整合具有重要意义。
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis
肿瘤(癌症)患者之家