Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China

Xiao Yi; Liu Shiyuan

doi:10.24920/003619

Your Location：

Home >

Browse articles >

Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China

Chinese Medical Sciences Journal;Chinese Medical Sciences Journal Vol. 34, Issue 2, Pages: 84-88(2019)

Perspectives | Updated：2024-04-10

- Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China
- Affiliations：
  
  海军军医大学长征医院放射科,上海 200003,中国
- Author bio：
  
  *,E-mail: cjr.liushiyuan@vip.163.com
- Funds：
- DOI：10.24920/003619
  CLC：
- Received：05 June 2019，
  
  Accepted：2019-6-10，
  
  Published Online：14 June 2019，
  
  Published：14 June 2019
- Accepted：
Scan QR Code
Xiao Yi, Liu Shiyuan. Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 84-88.
DOI：

Xiao Yi, Liu Shiyuan. Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 84-88. DOI： 10.24920/003619.

摘要

近年来

人工智能在医学影像领域得到快速发展

但医院、研究机构及企业方面的合作尚不充分

在技术成果转化和产品价值落地方面存在诸多问题。针对医学影像人工智能发展道路上的一些核心问题

中国医学影像AI产学研用创新联盟组织编撰了《中国医学影像AI白皮书》。本文介绍了中国医学影像人工智能的产学研用结合现状、临床需求及AI技术转化过程待解决的关键问题：鲁棒性、易用性和安全性。要真正实现医学影像AI产品应用价值

目前需要面对诸多挑战

如缺乏生产标准、数据脱敏标准、测试评价标准及适宜有效的政策监管等。医学影像AI向更深发展

需要核心算法的突破

需要医生的深度参与

需要资金投入和社会的耐心

更需要政府破解落地等诸多环节的难题。

Abstract

In recent years

artificial intelligence (AI) has developed rapidly in the field of medical imaging. However

the collaborations among hospitals

research institutes and enterprises are insufficient at the present

and there are various issues in technological transformation and value landing of products in this area. To solve the core problems in the developmental path of medical imaging AI

the Chinese Innovative Alliance of Industry

Education

Research and Application of Artificial Intelligence for Medical Imaging compiled the

White Paper on Medical Image AI in China

. This article introduces the current status of collaboration

the clinical demands for medical imaging AI technique

and the key points in AI technology transformation: robustness

usability and security. We are facing challenges of lacking industry standards

data desensitization standard

assessment system

as well as corresponding regulations and policies to realize the application values of AI products in medical imaging. Further development of AI in medical imaging requires breakthroughs of the core algorithm

deep involvement of doctors

input from capitals

patience from societies

and most importantly

the resolutions from government for multiple difficulties in links of landing the technology.

关键词

Keywords

references

Esteva A , Kuprel B , Novoa RA , et al . Dermatologist-level classification of skin cancer with deep neural networks . Nature 2017 ; 542 ( 7639 ): 115 - 8 . doi: DOI: 10.1038/nature21056 http://doi.org/10.1038/nature21056

Syed AB , Zoga AC . Artificial intelligence in radiology: current technology and future directions . Semin Musculoskelet Radiol 2018 ; 22 ( 5 ): 540 - 5 . doi: DOI: 10.1055/s-0038-1673383 http://doi.org/10.1055/s-0038-1673383 http://www.thieme-connect.de/DOI/DOI?10.1055/s-0038-1673383 http://www.thieme-connect.de/DOI/DOI?10.1055/s-0038-1673383

Tonutti M , Gras G , Yang GZ . A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery . Artif Intell Med 2017 ; 80 : 39 - 47 . doi: DOI: 10.1016/j.artmed.2017.07.004 http://doi.org/10.1016/j.artmed.2017.07.004 https://linkinghub.elsevier.com/retrieve/pii/S0933365716304687 https://linkinghub.elsevier.com/retrieve/pii/S0933365716304687

Krittanawong C , Zhang H , Wang Z , et al . Artificial intelligence in precision cardiovascular medicine . J Am Coll Cardiol 2017 ; 69 ( 21 ): 2657 - 64 . doi: DOI: 10.1016/j.jacc.2017.03.571 http://doi.org/10.1016/j.jacc.2017.03.571 https://linkinghub.elsevier.com/retrieve/pii/S0735109717368456 https://linkinghub.elsevier.com/retrieve/pii/S0735109717368456

Liu SY , Xiao Y . Challenges and opportunities of deep learning artificial intelligence in medical imaging . Chin J Radiol 2017 ; 51 ( 12 ): 899 - 901 . Chinese. doi: DOI: 10.3760/cma.j.issn.1005-1201.2017.12.002 http://doi.org/10.3760/cma.j.issn.1005-1201.2017.12.002 .

National Institutes for Food and Drug Control ; Chinese Society of Radiology Cardio-thoracic working group. Expert consensus on the rule and quality control of pulmonary nodule annotation based on thoracic CT . Chin J Radiol 2019 ; 53 ( 1 ): 9 - 15 . Chinese. doi: DOI: 10.3760/cma.j.issn.1005-1201.2019.01.004 http://doi.org/10.3760/cma.j.issn.1005-1201.2019.01.004

Chinese Society of Radiology Cardio-thoracic working group . Expert consensus on the imaging management of pulmonary subsolid nodule . Chin J Radiol 2015 ; 49 ( 4 ): 254 - 8 . Chinese. doi: DOI: 10.3760/cma.j.issn.1005-1201.2015.04.005 http://doi.org/10.3760/cma.j.issn.1005-1201.2015.04.005 .

Liu SY , Qian DH , Shen DG , et al . editor. Chinese Innovative Alliance of Industry, Education, Research and Application of Artificial Intelligence for Medical Imaging, CAIERA . The White Paper on Medical Imaging AI in China .https://vcbeat.top/Report/getReportFile/key/Nzgw. Released March 26, 2019 ; accessed June 3 , 2019 . https://vcbeat.top/Report/getReportFile/key/Nzgw https://vcbeat.top/Report/getReportFile/key/Nzgw

Zhang HM , Xiao Y , Hong N , et al . The report of status and needs in medical imaging artificial intelligence industry . Chin J Radiol 2019 ; 53 ( 6 ):507-11. Chinese. doi: DOI: 10.3760/cma.j.issn.1005-1201.2019.06.013 http://doi.org/10.3760/cma.j.issn.1005-1201.2019.06.013 .

Buda M , Saha A , Mazurowski MA . Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm . Comput Biol Med 2019 ; 109 : 218 - 25 . doi: DOI: 10.1016/j.compbiomed.2019.05.002 http://doi.org/10.1016/j.compbiomed.2019.05.002 https://linkinghub.elsevier.com/retrieve/pii/S0010482519301520 https://linkinghub.elsevier.com/retrieve/pii/S0010482519301520

Ehteshami Bejnordi B , Veta M , , et al . Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer . JAMA 2017 ; 318 ( 22 ): 2199 - 210 . doi: DOI: 10.1001/jama.2017.14585 http://doi.org/10.1001/jama.2017.14585 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2017.14585 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2017.14585

Sitek A , Wolfe JM . Assessing cancer risk from mammograms: deep learning is superior to conventional risk models . Radiology 2019 ; 190791 .doi: DOI: 10.1148/radiol.2019190791 http://doi.org/10.1148/radiol.2019190791 . Epub: 2019 May 7.

Liu K , Li Q , Ma J , et al . Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance . Radiol Artif Intell 2019 ; 1 ( 3 ). doi: DOI: 10.1148/ryai.2019180084 http://doi.org/10.1148/ryai.2019180084 . Epub: 2019 May 29.

Xiao Y , Liu SY . The state-of-the-art medical imaging artificial intelligence: challenges and strategies . Chin J Radiol 2019 ; 53 ( 1 ): 2 - 5 . Chinese. doi: DOI: 10.3760/cma.j.issn.1005-1201.2019.01.002 http://doi.org/10.3760/cma.j.issn.1005-1201.2019.01.002 .

Views

1187

Downloads

CSCD

Alert me when the article has been cited

Submit

Publicity Resources

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future

Semi-supervised Long-tail Endoscopic Image Classification

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Ontology: Footstone for Strong Artificial Intelligence

Related Author

Zhe Wu

Xiaofei Huo

Jingjing Lu

Xu Chen

Wang Qian

Shi Ying-huan

Mengjie Fang

Hailing Li

Related Institution

Department of Radiology, Beijing United Family Hospital

Department of Radiology, Fushun Central Hospital

State Key Laboratory for Novel Software Technology, Nanjing University

Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University

School of Artificial Intelligence, University of Chinese Academy of Sciences

⁰