Elemento Olivier, Leslie Christina, Lundin Johan, Tourassi Georgia
Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA.
Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Nat Rev Cancer. 2021 Dec;21(12):747-752. doi: 10.1038/s41568-021-00399-1. Epub 2021 Sep 17.
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闯入生物医学研究和医疗保健领域，这其中重要的包括癌症研究和肿瘤学，其潜在应用非常广泛。这些应用包括癌症的检测与诊断、亚型分类、癌症治疗的优化以及药物研发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，但要利用这一机会在癌症研究领域和临床领域充分实现人工智能的全部潜力，首先需要克服重大障碍。在这篇观点文章中，我们询问了四位专家关于如何在确保维持标准的同时开始实施人工智能，从而改变癌症诊断以及癌症患者的预后和治疗，并推动生物学发现的看法。
