人工智能（AI）技术的快速发展，在从高通量测序获得的大量可收集数据的背景下，已经带来了对癌症的前所未有的认识，并加速了医学精准治疗和个性化医疗的新时代的到来。然而，在临床肿瘤学实践中，各种AI模型取得的收益远未达到预期，特别是在临床治疗选择方案方面仍存在许多不确定性，这给AI在临床肿瘤学应用带来重大挑战。在本综述中，我们总结了AI的新方法、相关数据集和开放源代码软件，并展示如何整合它们来解决临床肿瘤学和癌症研究中的问题。我们重点介绍了利用AI识别不同抗肿瘤策略的原则和程序，包括治疗特定癌症、常规癌症治疗和癌症免疫疗法。此外，我们还强调了AI在临床肿瘤学转化中的当前挑战和方向。总体而言，我们希望本文能够为研究人员和临床医生提供更深入的理解，认识AI在精准癌症治疗中的作用和影响，并帮助AI更快地进入被接受的癌症指南。版权所有 © 2023 Elsevier Ltd.

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.Copyright © 2023. Published by Elsevier Ltd.