Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.

人工智能和机器学习技术正在进入生物医学研究和医疗保健领域，其中重要的是包括癌症研究和肿瘤学，这些领域的潜在应用非常广泛。其中包括癌症的检测和诊断、亚型分类、癌症治疗的优化以及药物发现中新治疗靶点的识别。虽然用于训练机器学习模型的大数据可能已经存在，但利用这个机会在癌症研究领域和临床领域实现人工智能的全部承诺首先需要克服重大障碍。在这篇观点文章中，