人工智能（AI）近年来在肿瘤学和相关专业中经历了爆炸式的增长。数据捕获的专业知识改善，数据聚合和分析能力的增加，以及基因组测序和相关生物学"组"技术成本的降低，为需求新型工具，以能够有效处理来自多个来源和不同类型的数据，而奠定了基础。这些进步以多模式方式为生物医学发现、诊断、预测、治疗和预防提供了价值。然而，虽然大数据和AI工具已经彻底改变了许多领域，但由于医学的复杂性和多维性，它在一定程度上滞后，从而导致在开发和验证能够推广到不同人群的解决方案时存在技术挑战。事实上，由于其实践过程中潜在的内在偏见和教育缺陷，算法的内在偏见和误学现象越来越引人关注。重要的是，AI可能复制出生成这些算法的人类的潜意识偏见。因此，为避免恶化现有的健康不平等现象，有必要采取思考周全、透明开放和包容的方法，涉及到在癌症护理的整个过程中解决算法设计和实施中的偏见问题。本文提供了对AI在癌症护理中的主要应用的广泛概述，重点关注癌症研究和精准医学。讨论了AI在临床环境中实施所面临的主要挑战。在促进癌症健康公平的光线下，提供了减轻偏见的潜在可行方案。版权所有© 2022 Elsevier Ltd.

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.Copyright © 2022 Elsevier Ltd. All rights reserved.