Artificial intelligence in radiation oncology

被引:185
|
作者
Huynh, Elizabeth [1 ,2 ]
Hosny, Ahmed [1 ,2 ]
Guthier, Christian [2 ]
Bitterman, Danielle S. [1 ,2 ,3 ]
Petit, Steven E. [4 ]
Haas-Kogan, Daphne A. [1 ,2 ]
Kann, Benjamin [1 ,2 ]
Aert, Hugo J. W. L. [1 ,2 ,5 ,6 ,7 ]
Mak, Raymond H. [1 ,2 ]
机构
[1] Harvard Med Sch, Brigham & Womens Hosp, Artificial Intelligence Med AIM Program, Boston, MA 02115 USA
[2] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiat Oncol, Dana Farber Canc Inst, Boston, MA 02115 USA
[3] Harvard Med Sch, Computat Hlth Informat Program, Boston Childrens Hosp, Boston, MA 02115 USA
[4] Erasmus MC, Dept Radiat Oncol, Canc Inst, Rotterdam, Netherlands
[5] Harvard Med Sch, Brigham & Womens Hosp, Dana Farber Canc Inst, Dept Radiol, Boston, MA 02115 USA
[6] Maastricht Univ, Dept Radiol & Nucl Med, CARIM, Maastricht, Netherlands
[7] Maastricht Univ, GROW, Maastricht, Netherlands
关键词
ADAPTIVE NEURAL-NETWORK; CELL LUNG-CANCER; SURVIVAL PREDICTION; MEDICAL PHYSICISTS; RADIOTHERAPY; THERAPY; QUALITY; HEAD; SYSTEM; MOTION;
D O I
10.1038/s41571-020-0417-8
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
The possible uses of artificial intelligence (AI) in radiation oncology are diverse and wide ranging. Herein, the authors discuss the potential applications of AI at each step of the radiation oncology workflow, which might improve the efficiency and overall quality of radiation therapy for patients with cancer. The authors also describe the associated challenges and provide their perspective on how AI platforms might change the roles of radiation oncology medical professionals. Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.
引用
收藏
页码:771 / 781
页数:11
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