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Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.

Artificial intelligence in radiology.

Author information

1
Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
2
Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
3
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
4
Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA.
5
Department of Radiology, New York Presbyterian Hospital, New York, NY, USA.
6
Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. Hugo_Aerts@dfci.harvard.edu.
7
Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Hugo_Aerts@dfci.harvard.edu.

Abstract

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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