Artificial intelligence in oncology: From bench to clinic

被引:19
作者
Elkhader, Jamal [1 ,2 ,3 ,4 ]
Elemento, Olivier [1 ,2 ,3 ,4 ]
机构
[1] Weill Cornell Med, HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud, Dept Physiol & Biophys, New York, NY 10021 USA
[2] Weill Cornell Med, Caryl & Israel Englander Inst Precis Med, New York, NY 10021 USA
[3] Weill Cornell Med, Sandra & Edward Meyer Canc Ctr, New York, NY 10065 USA
[4] Triinst Training Program Computat Biol & Med, New York, NY 10065 USA
基金
美国国家卫生研究院;
关键词
Artificial intelligence; Cancer; Machine learning; Review; ELECTRONIC HEALTH RECORDS; IMAGE DATABASE CONSORTIUM; CIRCULATING TUMOR DNA; LEARNING APPLICATIONS; GENETIC ALGORITHMS; LIQUID BIOPSIES; LUNG NODULES; PAN-CANCER; DEEP; CLASSIFICATION;
D O I
10.1016/j.semcancer.2021.04.013
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproduc-ibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.
引用
收藏
页码:113 / 128
页数:16
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