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.

肿瘤学实践需要分析和综合大量数据。从确定患者接受治疗资格的检查到治疗后监测，从业者必须根据他们对手头信息的最佳理解不断地权衡、评估和权衡决策。这些复杂的多因素决策有巨大的机会受益于数据驱动的机器学习 (ML) 方法，从而推动人工智能 (AI) 的机遇。在过去的五年里，我们看到人工智能从单纯的一个有希望的机会转变为用于前瞻性试验。在这里，我们回顾了人工智能在临床试验中的最新成果，这些成果推动了对可操作结果的改进预测，例如预测急症护理就诊、短期死亡率和病理性结外扩展。然后，我们停下来思考这些人工智能模型如何提出与读者可能更熟悉的传统统计模型不同的问题；那么读者应该如何概念化和解释他们不熟悉的人工智能模型。最后，我们相信人工智能在肿瘤学领域的未来前景光明，着眼于让数据通过无监督学习和生成模型为我们提供信息，而不是要求人工智能执行特定功能。