在精准肿瘤学时代，包括影像放射组学和各种分子生物标志物等多组学数据越来越多地用于确诊和治疗。人工智能（AI）技术，包括机器学习（ML）和深度学习（DL）技术与多组学数据的指数级增长相结合，可能具有革命性的潜力，可用于癌症亚型划分、风险分层、预后预测、预测和临床决策。本文首先介绍了多组学数据的不同类别及其在诊断和治疗中的作用。其次，展示了基于AI的数据融合方法和建模方法以及不同的验证方案。第三，展示了多组学研究在肿瘤学中的应用和实例。最后，讨论了异质性数据集、组学数据的可用性以及研究的验证等方面的挑战。从多组学研究向真实临床的转变仍然需要不断努力，包括统一标准化组学数据收集与分析、构建用于数据共享和存储的计算基础设施、开发改进数据融合和可解释性的先进方法，最终进行大规模前瞻性临床试验，以弥合研究结果和临床效益之间的差距。

Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making.In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.