Artificial intelligence in cancer research, diagnosis and therapy

被引:125
作者
Elemento, Olivier [1 ]
Leslie, Christina [2 ]
Lundin, Johan [3 ,4 ,5 ]
Tourassi, Georgia [6 ]
机构
[1] Cornell Univ, Weill Cornell Med, Caryl & Israel Englander Inst Precis Med, New York, NY 10021 USA
[2] Mem Sloan Kettering Canc Ctr, Computat & Syst Biol Program, 1275 York Ave, New York, NY 10021 USA
[3] Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden
[4] Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland
[5] Univ Helsinki, ICAN Digital Precis Canc Med Flagship, Helsinki, Finland
[6] Oak Ridge Natl Lab, Natl Ctr Computat Sci, Oak Ridge, TN 37830 USA
基金
瑞典研究理事会;
关键词
LEARNING ALGORITHM; DEEP; PERFORMANCE; VALIDATION; CARE; GO; AI;
D O I
10.1038/s41568-021-00399-1
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
In this Viewpoint article, we asked four experts to share their thoughts on the implementation of artificial intelligence and machine learning techniques into cancer research and care, and how to separate the hope from the hype to overcome the challenges ahead. Standfirst 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.
引用
收藏
页码:747 / 752
页数:6
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