Zheng-yu JIN. Prospects and Challenges:when Medical Imaging Meets Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 2-4. doi: 10.3969/j.issn.1674-9081.2018.01.001
Citation: Zheng-yu JIN. Prospects and Challenges:when Medical Imaging Meets Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 2-4. doi: 10.3969/j.issn.1674-9081.2018.01.001

Prospects and Challenges:when Medical Imaging Meets Artificial Intelligence

doi: 10.3969/j.issn.1674-9081.2018.01.001
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  • Author Bio:

    JIN Zheng-yu Tel:010-69155442, E-mail:jin_zhengyu@163.com

  • Received Date: 2017-12-21
  • Publish Date: 2018-01-30
  • With the rapid development of artificial intelligence, there is a general consensus of opinion that radiologists' workload can be dramatically reduced with the aid of intelligent image recognition. On the issues of comprehensive diagnosis and treatment, however, there is no certain answer whether or not artificial intelligence can provide better suggestions and comments. Currently, in China, the artificially intelligent imaging technique is mainly focused on simple image recognition, but there is a lack of experience in the accumulation of medical data and the analysis of radiological reports. The mode of integrating artificial intelligence with medical imaging science has just begun. We believe that the progress of science and technology will continue to be the engine of human civilization.
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  • [1] Kahn CE Jr. From images to actions:opportunities for artificial intelligence in radiology[J]. Radiology, 2017, 285:719-720. doi:  10.1148/radiol.2017171734
    [2] Turing AM. Computing machinery and intelligence[J]. Mind, 1950, 59:433-460. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=HighWire000002027644
    [3] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521:436-444. doi:  10.1038/nature14539
    [4] Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316:2402-2410. doi:  10.1001/jama.2016.17216
    [5] 2018-2024年中国人工智能+医疗影像行业市场研究及投资前景预测报告[R]. http://www.chyxx.com/research/201710/578114.html.
    [6] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313:504-507. doi:  10.1126/science.1127647
    [7] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542:115-118. doi:  10.1038/nature21056
    [8] Komeda Y, Handa H, Watanabe T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification:preliminary experience[J]. Oncology, 2017, 93:30-34. doi:  10.1159/000481227
    [9] Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318:2199-2210. doi:  10.1001/jama.2017.14585
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