Rapid Layout and Development Strategy of Hospital Artificial Intelligence During the COVID-19 Pandemic

Xinhua Chen, Jianwen Jiang, Hua Zhou, Haiyang Xie, Lin Zhou, Danjing Guo, Chen Xue, Weiwei Zhu, Jianying Zhou, Shusen Zheng

Strategic Study of CAE ›› 2020, Vol. 22 ›› Issue (2) : 130-137.

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Strategic Study of CAE ›› 2020, Vol. 22 ›› Issue (2) : 130-137. DOI: 10.15302/J-SSCAE-2020.02.021
Engineering Management
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Rapid Layout and Development Strategy of Hospital Artificial Intelligence During the COVID-19 Pandemic

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Abstract

This study aims to explore the application of artificial intelligence (AI) in the context of coronavirus disease 2019 (COVID-19) pandemic, with a view for promoting the top-level design and rapid layout of hospital-orientated AI application, in order to provide a new path for the Healthy China Initiative. Since the outbreak of the COVID-19 epidemic, China has conducted epidemic prevention and control using the strength of the whole country, and simultaneously developed global cooperation by donating medical supplies, sending medical teams, and sharing treatment experience and high technologies. In fighting against the epidemic, the front-line medical staff has obtained valuable experience in medical AI application. AI has played an outstanding role in the anti-epidemic frontline and indicates the urgent and strategic demands for medical AI. After reviewing the medical AI application status and development direction, we suggest that China should make a comprehensive layout of AI application in hospitals nationwide and cultivate a prosperous medical AI ecology, thus to lay a key foundation for future hospital construction in China. To this end, the government should make an overall top-level design for medical AI application and shift forward interventions so as to change the passive situation. It also should scientifically coordinate resources, strengthen hardware construction, improve the databank, and improve the guarantee system for professional teams. Armed with medical AI, hospitals can fight against large-scale acute respiratory infectious diseases with better efficiency.

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hospital construction / coronavirus disease 2019 (COVID-19) / artificial intelligence (AI) / application status / development direction

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Xinhua Chen, Jianwen Jiang, Hua Zhou, Haiyang Xie, Lin Zhou, Danjing Guo, Chen Xue, Weiwei Zhu, Jianying Zhou, Shusen Zheng. Rapid Layout and Development Strategy of Hospital Artificial Intelligence During the COVID-19 Pandemic. Strategic Study of CAE, 2020, 22(2): 130‒137 https://doi.org/10.15302/J-SSCAE-2020.02.021

References

[1]
曹艳林, 王将军, 陈璞, 等. 人工智能对医疗服务的机遇与挑战 [J]. 中国医院, 2018, 22(6): 25–28. Cao Y L, Wang J J, Chen P, et al. Opportunities and challenges of artificial intelligence in medical care [J]. Chinese Hospitals, 2018, 22(6): 25–28.
[2]
于观贞, 刘西洋, 张彦春, 等. 人工智能在临床医学中的应用与 思考 [J]. 第二军医大学学报, 2018, 39(4): 358–365. Yu G Z, Liu X Y, Zhang Y C, et al. Artificial intelligence in clinical medicine: Application and thinking [J]. Academic Journal of Second Military Medical University, 2018, 39(4): 358–365.
[3]
Guo Q, Wang C H, Wang P H, et al. Host and infectivity prediction of 2019 novel coronavirus using deep learning algorithm [EB/OL]. (2020-01-22) [2020-03-15]. https://www.biorxiv.org/content/10.11 01/2020.01.21.914044v1.full.pdf.
[4]
Zhou P, Yang X L, Wang X G, et al. Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin [EB/OL]. (2020-01-23) [2020- 03-15]. https://www.biorxiv.org/content/10.1101/2020.01.22.9149 52v2.
[5]
Lam T T, Shum M H, Zhu H C, et al. Identificationof 2019-nCoV related coronaviruses in Malayan pangolins in southern China [EB/ OL]. (2020-02-13) [2020-03-15]. https://www.biorxiv.org/content/ 10.1101/2020.02.13.945485v1.full.pdf.
[6]
李兴臣. 人工智能医疗服务的法律责任 [J]. 医学与法学, 2018, 10(4): 8–12. Li X C. Legal liability of artificial intelligence medical service [J]. Medicine & Jurisprudence, 2018, 10(4): 14–18.
[7]
马治国, 徐济宽. 人工智能发展的潜在风险及法律防控监管 [J]. 北京工业大学学报(社会科学版), 2018, 18(6): 65–71. Ma Z G, Xu J K. Legal precaution and supervision about the potential risks in artificial intelligence development [J]. Journal of Beijing University of Technology (Social Sciences Edition), 2018, 18(6): 65–71.
[8]
蒋洁. 人工智能应用的风险评估与应对策略 [J]. 图书与情报, 2017 (6): 117–123. Jiang J. Risk assessments and countermeasures of AI applications [J]. Library and Information, 2017 (6): 117–123.
[9]
董雪. 人工智能在信息安全风险评估中的应用研究 [J]. 信息系 统工程, 2019 (3): 78. Dong X, Research on application of artificial intelligence in information security risk assessment [J].China CIO News, 2019 (3): 78.
[10]
于鹤, 赵稳兴. 计算机辅助诊断技术在病理学中的应用进展 [J]. 诊断病理学杂志, 2018, 25(3): 223–226. Yu H, Zhao W X. Application progress of computer-aided diagnosis technology in pathology [J]. Chinese Journal of Diagnostic Pathology, 2018, 25(3): 223–226.
[11]
于观贞, 魏培莲, 陈颖, 等. 人工智能在肿瘤病理诊断和评估中 的应用与思考 [J]. 第二军医大学学报, 2017, 38(11): 6–11. Yu G Z, Wei P L, Chen Y, et al. Artificial intelligence in pathological diagnosis and assessment of human solid tumor: Application and thinking [J]. Academic Journal of Second Military Medical University, 2017, 38(11): 6–11.
[12]
闫雯, 李楠楠, 张益肇, 等. 人工智能时代的病理组学 [J]. 临床 与实验病理学杂志, 2018, 34(6): 661–664. Yan W, Li N N, Zhang Y Z, et al. Pathology in the age of artificial intelligence [J]. Chinese Journal of Clinical and Experimental Pathology, 2018, 34(6): 661–664.
[13]
周瑞泉, 纪洪辰, 刘荣. 智能医学影像识别研究现状与展望 [J]. 第二军医大学学报, 2018, 39(8): 917–922. Zhou R Q, Ji H C, Liu R. Intelligent medical image recognition: Progress and prospect [J]. Academic Journal of Second Military Medical University, 2018, 39(8): 917–922.
[14]
祁瑞娟, 吕伟通. 人工智能辅助诊断技术在医疗领域的作用与 挑战 [J]. 中国医疗器械信息, 2018, 24(16): 27–28. Qi R J, Lv W T. The role and challenges of artificial intelligence-assisted diagnostics in the medical field [J]. China Medical Device Information, 2018, 24(16): 27–28.
[15]
程京, 邢婉丽. 医疗器械与新型穿戴式医疗设备的发展战略研 究 [J]. 中国工程科学, 2017, 19(2): 68–71. Cheng J, Xing W L. Research on the development strategy of medical devices and new wearable devices [J]. Strategic Study of CAE, 2017, 19(2): 68–71.
[16]
Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11–20.
[17]
Turan M, Almalioglu Y, Konukoglu E, et al. A deep learning based 6 degree-of-freedom localization method for endoscopic capsule robots [EB/OL]. (2017-05-15) [2020-02-15]. https://arxiv.org/ abs/1705.05435v1.
[18]
Wu G, Kim M, Wang Q, et al. Scalable high performance image registration framework by unsupervised deep feature representations learning [J]. IEEE Transactions on Biomedical Engineering, 2016, 63(7): 1505–1516.
Funding
Major National Science and Technology Projects “Prevention and Control of Major Infectious Diseases including AIDS and Viral Hepatitis” (2018ZX10301201); CAE Advisory Project “Research on the Application and Development Strategy of Artificial Intelligence in the Field of Medicine and Healthcare” (2019- ZD-06); Zhejiang University Education Foundation Project (2020XGZX063)
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