肿瘤学实践要求分析和综合大量数据，从病人的工作来确定适应症，到接受的治疗方案，再到治疗后的监测，从业者必须不断地在他们对手头信息的最佳理解基础上进行抉择、评估和权衡决策。这些复杂的、多因素的决策有着巨大的机会从数据驱动的机器学习（ML）方法中受益，以推动人工智能（AI）的机会。在过去的5年里，我们发现AI从一个仅仅有希望的机会变成了在前瞻性试验中被使用的技术。在这里，我们回顾了AI在临床试验中的最新努力，这些努力在改善可行性结局的预测方面起到了积极的推动作用，比如预测急性护理访问、短期死亡率和病理学外淋巴结侵犯范围。然后，我们暂停并反思这些AI模型提出了一个与传统统计模型有所不同的问题，读者们可能对这些AI模型不太熟悉。那么，读者应如何理解和解释那些不太熟悉的AI模型呢？最后，我们讨论了我们认为肿瘤学中有希望的未来机会，目光放在让数据通过无监督学习和生成模型来告诉我们，而不是要求AI执行特定的功能。版权所有 © 2023 Elsevier Inc. 保留所有权利。

The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh decision-making based on their best understanding of information at hand. These complex, multifactorial decisions have a tremendous opportunity to benefit from data-driven machine learning (ML) methods to drive opportunities in artificial intelligence (AI). Within the past 5 years, we have seen AI move from simply a promising opportunity to being used in prospective trials. Here, we review recent efforts of AI in clinical trials that have moved the needle towards improved prediction of actionable outcomes, such as predicting acute care visits, short term mortality, and pathologic extranodal extension. We then pause and reflect on how these AI models ask a different question than traditional statistics models that readers may be more familiar with; how then should readers conceptualize and interpret AI models that they are not as familiar with. We end with what we believe are promising future opportunities for AI in oncology, with an eye towards allowing the data to inform us through unsupervised learning and generative models, rather than asking AI to perform specific functions.Copyright © 2023 Elsevier Inc. All rights reserved.