The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI’s scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.

以数据驱动识别疾病状态和治疗方案是精准肿瘤学面临的一项关键挑战。人工智能 (AI) 为增强实验室和诊所的此类预测能力提供了独特的机会。人工智能，包括其最著名的研究分支——机器学习，在实现精准肿瘤学方面具有巨大的潜力，远远超出相对知名的模式识别应用，例如单源组学或成像数据集的监督分类。这一观点强调了该方向的关键进展和挑战。此外，它认为人工智能的研究范围和深度需要扩大，以在精准肿瘤学方面取得突破性进展。