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中华损伤与修复杂志(电子版) ›› 2019, Vol. 14 ›› Issue (06) : 406 -409. doi: 10.3877/cma.j.issn.1673-9450.2019.06.002

所属专题: 文献

学术争鸣

人工智能决策系统在烧伤领域应用的主要瓶颈与解决途径
张勤1,()   
  1. 1. 200025 上海交通大学医学院附属瑞金医院烧伤整形科
  • 收稿日期:2019-10-15 出版日期:2019-12-01
  • 通信作者: 张勤
  • 基金资助:
    国家自然科学基金面上项目(81971832)

Main bottlenecks and solutions of artificial intelligence decision system in burn field

Qin Zhang1,()   

  1. 1. Department of Burns and Plastic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  • Received:2019-10-15 Published:2019-12-01
  • Corresponding author: Qin Zhang
  • About author:
    Corresponding author: Zhang Qin, Email:
引用本文:

张勤. 人工智能决策系统在烧伤领域应用的主要瓶颈与解决途径[J]. 中华损伤与修复杂志(电子版), 2019, 14(06): 406-409.

Qin Zhang. Main bottlenecks and solutions of artificial intelligence decision system in burn field[J]. Chinese Journal of Injury Repair and Wound Healing(Electronic Edition), 2019, 14(06): 406-409.

人工智能决策进入临床应用面临着3个瓶颈:烧伤医疗大数据、深度学习和医学伦理。如何在较长时间采集过程中保持数据稳定并选取科学方法加以分析与评判;机器人深度学习,学习什么与分析什么、如何克服人工智能机器与医师培养的长期性差异;在大数据与人工智能迅速发展形势下伦理短板日益体现。解决这3个问题的主要途径:应主动与数据科学家共同搭建数据模型平台,并制定数据采纳基本线路图,与此同时,由全国烧伤委员会制定大数据及人工智能的伦理规则刻不容缓。

Artificial intelligence decision-making faces 3 bottlenecks before it goes into burn treatment: big data, deep learning and medical ethics. How to maintain data stabiliy in long-term acquisition and select scientific methods for analysis and judgement. Which kinds of material should be studied and analyzed by deeping learning. How to overcome the long-term difference between artificial intelligence machine and doctor training. Under the situation of rapid development of big data and artificial intelligence, the ethical shortcomings are increasingly reflected. The main way to solve the 3 problems is: the initiative to build a data model platform together with data scientists should be taken, and the basic circuit diagram of data adoption should be developed. Meanwhile, it is urgent for the national committee of burns to formulate the ethical rules of big data and artificial intelligence.

[1]
李海航,包振兴,刘晓彬,等. 人工智能在烧伤领域的应用研究进展[J]. 中华烧伤杂志,2018, 34 (4): 246-248.
[2]
Celi LA, Hinske LC, Alterovitz G, et al. An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study[J]. Crit Care, 2008, 12 (6): R151.
[3]
Golas SB, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data[J]. BMC Med Inform Decis Mak, 2018, 18(1): 44.
[4]
Yoshioka A. Use of randomisation in the Medical Research Council's clinical trial of streptomycin in pulmonary tuberculosis in the 1940s[J]. BMJ, 1998, 317(7167): 1220-1223.
[5]
Jones DS, Podolsky SH. The history and fate of the gold standard[J]. Lancet, 2015, 385(9977): 1502-1503.
[6]
Feinstein AR, Horwitz RI. Double standards, scientific methods, and epidemiologic research[J].N Engl J Med, 1982, 307(26): 1611-1617.
[7]
Howard R, Lovestone S, Levy R. Ernest Saunders: diagnostic dilemma[J]. BMJ, 1992, 304(6841): 1568-1569.
[8]
Bhagat S, Kapatkar V, Katare S, et al. Potential Risks and Mitigation Strategies During the Conduct of a Clinical Trial: An Industry Perspective[J]. Rev Recent Clin Trials, 2018, 13(1): 52-60.
[9]
Celi LA, Lokhandwala S, Montgomery R, et al. Datathons and Software to Promote Reproducible Research[J]. J Med Internet Res, 2016, 18(8): e230.
[10]
张政波,薛万国,曹德森,等. 急救大数据与Datathon活动[J]. 中华危重病急救医学,2018, 30(6): 603-605.
[11]
Adama M, Ng EYK, Tan JH, et al. Computer aided diagnosis of diabetic foot using infrared thermograhpy: A review[J]. Comput Biol Med, 2017, 91: 326-336.
[12]
Shortliffe EH, Sepúlveda MJ. Clinical Decision Support in the Era of Artificial Intelligence[J]. JAMA, 2018, 320(21): 2199-2200.
[13]
Salerno J, Knoppers BM, Lee LM, et al. Ethics, big data and computing in epidemiology and public health[J]. Ann Epidemiol, 2017, 27(5): 297-301.
[14]
Gordijn B, Have HT. Technology and dementia[J]. Med Health Care Philos, 2016, 19(3): 339-340
[15]
Van den Hoven J, Lokhorst GJ, Van de Poel I. Engineering and the problem of moral overload[J]. Sci Eng Ethics, 2012, 18(1): 143-155.
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