Artificial intelligence in radiation oncology

被引:0
|
作者
Elizabeth Huynh
Ahmed Hosny
Christian Guthier
Danielle S. Bitterman
Steven F. Petit
Daphne A. Haas-Kogan
Benjamin Kann
Hugo J. W. L. Aerts
Raymond H. Mak
机构
[1] Brigham and Women’s Hospital,Artificial Intelligence in Medicine (AIM) Program
[2] Harvard Medical School,Department of Radiation Oncology, Dana
[3] Brigham and Women’s Hospital,Farber Cancer Institute
[4] Harvard Medical School,Computational Health Informatics Program
[5] Boston Children’s Hospital,Department of Radiation Oncology
[6] Harvard Medical School,Department of Radiology, Dana
[7] Erasmus MC Cancer Institute,Farber Cancer Institute
[8] Brigham and Women’s Hospital,Department of Radiology and Nuclear Medicine
[9] Harvard Medical School,undefined
[10] CARIM & GROW,undefined
[11] Maastricht University,undefined
来源
Nature Reviews Clinical Oncology | 2020年 / 17卷
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摘要
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.
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页码:771 / 781
页数:10
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