• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

利用人工智能技术抗击新冠疫情：综述

Using artificial intelligence technology to fight COVID-19: a review.

作者信息

Peng Yong, Liu Enbin, Peng Shanbi, Chen Qikun, Li Dangjian, Lian Dianpeng

机构信息

Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China.

School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500 China.

出版信息

Artif Intell Rev. 2022;55(6):4941-4977. doi: 10.1007/s10462-021-10106-z. Epub 2022 Jan 3.

DOI:10.1007/s10462-021-10106-z
PMID:35002010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8720541/
Abstract

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

摘要

2019年12月下旬，发现了一种新型冠状病毒，该病毒后来被命名为严重急性呼吸综合征冠状病毒2（SARS-CoV-2）。自发现以来，该病毒已在全球传播，截至2021年4月15日已造成2975875人死亡，并对我们的卫生系统和经济产生了巨大影响。如何抑制新冠肺炎的持续传播是许多科学家和研究人员的主要任务。人工智能技术的引入为抑制新型冠状病毒做出了巨大贡献。本文通过大量文献研究，从识别、预测和研发三个主要方面探讨了人工智能技术在抑制冠状病毒方面的主要应用，并提出了当前面临的主要挑战和可能的发展方向。结果表明，将人工智能技术与多种新技术相结合来预测和识别新冠肺炎患者是一种有效的措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/2876331e6fa9/10462_2021_10106_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/9d647c7f03e4/10462_2021_10106_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/6f8a7d28eac1/10462_2021_10106_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/5f03096d5ce7/10462_2021_10106_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/ffbd3701e843/10462_2021_10106_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/9e1bc24cf66f/10462_2021_10106_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/e30def4418f8/10462_2021_10106_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/3d2126535399/10462_2021_10106_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/00bc1647f453/10462_2021_10106_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/1f2ee3b61e19/10462_2021_10106_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/3b6bf2a36169/10462_2021_10106_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/2037e87323a3/10462_2021_10106_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/deb867a94f81/10462_2021_10106_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/90a0875c28cc/10462_2021_10106_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/bce367dd0611/10462_2021_10106_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/2876331e6fa9/10462_2021_10106_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/9d647c7f03e4/10462_2021_10106_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/6f8a7d28eac1/10462_2021_10106_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/5f03096d5ce7/10462_2021_10106_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/ffbd3701e843/10462_2021_10106_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/9e1bc24cf66f/10462_2021_10106_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/e30def4418f8/10462_2021_10106_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/3d2126535399/10462_2021_10106_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/00bc1647f453/10462_2021_10106_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/1f2ee3b61e19/10462_2021_10106_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/3b6bf2a36169/10462_2021_10106_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/2037e87323a3/10462_2021_10106_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/deb867a94f81/10462_2021_10106_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/90a0875c28cc/10462_2021_10106_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/bce367dd0611/10462_2021_10106_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcfa/8720541/2876331e6fa9/10462_2021_10106_Fig15_HTML.jpg

相似文献

1
Using artificial intelligence technology to fight COVID-19: a review.利用人工智能技术抗击新冠疫情：综述
Artif Intell Rev. 2022;55(6):4941-4977. doi: 10.1007/s10462-021-10106-z. Epub 2022 Jan 3.
2
Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.基于人工智能和智能物联网预防COVID-19传播的方法：范围综述
J Med Internet Res. 2020 Aug 10;22(8):e19104. doi: 10.2196/19104.
3
Artificial Intelligence (AI) applications for COVID-19 pandemic.用于2019冠状病毒病大流行的人工智能（AI）应用程序。
Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14.
4
[Applications of separation technology in novel coronavirus research, epidemic prevention and detection].[分离技术在新型冠状病毒研究、疫情防控与检测中的应用]
Se Pu. 2021 Jul 8;39(7):679-685. doi: 10.3724/SP.J.1123.2021.03022.
5
Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.大数据和人工智能在中国 COVID-19 预防、诊断、治疗和管理决策中的应用。
J Med Syst. 2021 Jul 24;45(9):84. doi: 10.1007/s10916-021-01757-0.
6
Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare.将数字技术与公共卫生相结合以抗击新冠疫情：数字医疗的关键技术、应用、挑战和展望。
Int J Environ Res Public Health. 2021 Jun 4;18(11):6053. doi: 10.3390/ijerph18116053.
7
Current Strategies of Antiviral Drug Discovery for COVID-19.新型冠状病毒肺炎抗病毒药物研发的当前策略
Front Mol Biosci. 2021 May 13;8:671263. doi: 10.3389/fmolb.2021.671263. eCollection 2021.
8
Blockchain technologies to mitigate COVID-19 challenges: A scoping review.区块链技术应对新冠疫情挑战：一项范围综述
Comput Methods Programs Biomed Update. 2021;1:100001. doi: 10.1016/j.cmpbup.2020.100001. Epub 2020 Dec 14.
9
Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.基于区块链和人工智能的应对类冠状病毒（COVID-19）疫情的解决方案：一项综述。
IEEE Access. 2021 Jun 30;9:95730-95753. doi: 10.1109/ACCESS.2021.3093633. eCollection 2021.
10
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.机器学习和人工智能在2019冠状病毒病（严重急性呼吸综合征冠状病毒2）大流行中的应用：综述
Chaos Solitons Fractals. 2020 Oct;139:110059. doi: 10.1016/j.chaos.2020.110059. Epub 2020 Jun 25.

引用本文的文献

1
Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts.在医疗保健领域对人工智能信任达成共识：国际专家小组的建议。
J Med Internet Res. 2025 Feb 19;27:e56306. doi: 10.2196/56306.
2
Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.人工智能辅助医学研究：医学人工智能综述
Diagnostics (Basel). 2024 Jul 9;14(14):1472. doi: 10.3390/diagnostics14141472.
3
Identification of Dual Inhibitors Targeting Main Protease (M) and Cathepsin L as Potential Anti-SARS-CoV-2 Agents.

本文引用的文献

1
Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing.利用机器学习和云计算预测新冠疫情的发展与趋势。
Internet Things (Amst). 2020 Sep;11:100222. doi: 10.1016/j.iot.2020.100222. Epub 2020 May 12.
2
A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration.用于开放科学研究的大规模COVID-19推特聊天数据集——一项国际合作。
Epidemiologia (Basel). 2021 Aug 5;2(3):315-324. doi: 10.3390/epidemiologia2030024.
3
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts.
鉴定靶向主要蛋白酶（M）和组织蛋白酶L的双重抑制剂作为潜在的抗SARS-CoV-2药物
ACS Med Chem Lett. 2024 Mar 1;15(5):602-609. doi: 10.1021/acsmedchemlett.3c00562. eCollection 2024 May 9.
4
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis.学术出版商关于人工智能使用的指南：一个基于 ChatGPT 的主题分析。
F1000Res. 2024 Jan 16;12:1398. doi: 10.12688/f1000research.142411.2. eCollection 2023.
5
Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.深度学习预测模型对 COVID-19 严重程度的诊断准确性：系统评价和荟萃分析。
J Med Internet Res. 2023 Jul 21;25:e46340. doi: 10.2196/46340.
6
A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.关于大流行患者监测系统的全面综述：使能技术、机遇与研究挑战
Wirel Pers Commun. 2023 Jun 2:1-48. doi: 10.1007/s11277-023-10535-9.
7
Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.用于在预防、检测和服务提供方法中抗击新冠疫情的计算机辅助方法。
Neural Comput Appl. 2023;35(20):14739-14778. doi: 10.1007/s00521-023-08612-y. Epub 2023 May 5.
8
Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic.无人机技术在缓解新冠疫情中的作用。
Micromachines (Basel). 2022 Sep 25;13(10):1593. doi: 10.3390/mi13101593.
9
Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.心胸成像中的机器学习与深度学习：一项范围综述
Diagnostics (Basel). 2022 Oct 17;12(10):2512. doi: 10.3390/diagnostics12102512.
10
Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.用于新冠病毒检测的机器学习系统综合调查
J Imaging. 2022 Sep 30;8(10):267. doi: 10.3390/jimaging8100267.
用于冠状病毒（COVID-19）大流行的人工智能（AI）与大数据：技术现状综述
IEEE Access. 2020 Jul 15;8:130820-130839. doi: 10.1109/ACCESS.2020.3009328. eCollection 2020.
4
Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model.基于物联网的 COVID-19 及其他传染病接触者追踪匿名保护模型
IEEE Access. 2020 Aug 31;8:159402-159414. doi: 10.1109/ACCESS.2020.3020513. eCollection 2020.
5
A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications.一种基于区块链和人工智能的、以患者为中心的抗击新冠疫情医疗系统：机遇与应用
Healthcare (Basel). 2021 Aug 8;9(8):1019. doi: 10.3390/healthcare9081019.
6
Masks use and facial dermatitis during COVID-19 outbreak: is there a difference between CE and non-CE approved masks? Multi-center, real-life data from a large Italian cohort.口罩使用与 COVID-19 大流行期间的面部皮炎：CE 认证与非 CE 认证口罩之间是否存在差异？来自意大利大型队列的多中心真实世界数据。
Ital J Dermatol Venerol. 2021 Apr;156(2):220-225. doi: 10.23736/S2784-8671.21.06895-4.
7
An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19.一种综合药物重定位框架发现了一种针对 COVID-19 的潜在治疗药物。
Signal Transduct Target Ther. 2021 Apr 24;6(1):165. doi: 10.1038/s41392-021-00568-6.
8
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).利用 CT 图像进行冠状病毒病（COVID-19）筛查的深度学习算法。
Eur Radiol. 2021 Aug;31(8):6096-6104. doi: 10.1007/s00330-021-07715-1. Epub 2021 Feb 24.
9
COVID-19 related masks increase severity of both acne (maskne) and rosacea (mask rosacea): Multi-center, real-life, telemedical, and observational prospective study.新冠疫情相关口罩会加重痤疮（“口罩痘”）和玫瑰痤疮（“口罩玫瑰痤疮”）的病情：多中心、真实生活、远程医疗及观察性前瞻性研究。
Dermatol Ther. 2021 Mar;34(2):e14848. doi: 10.1111/dth.14848. Epub 2021 Feb 19.
10
Mask-induced Koebner phenomenon and its clinical phenotypes: A multicenter, real-life study focusing on 873 dermatological consultations during COVID-19 pandemics.口罩诱发的 Koebner 现象及其临床表型：一项多中心、真实世界研究，在 COVID-19 大流行期间聚焦 873 次皮肤科就诊。
Dermatol Ther. 2021 Mar;34(2):e14823. doi: 10.1111/dth.14823. Epub 2021 Feb 8.