Artificial Intelligence in the Fight Against COVID-19: Scoping Review.
Artificial Intelligence in the Fight Against COVID-19: Scoping Review.
复制标题
与Covid-19的斗争中的人工智能：范围审查。
DOI：
10.2196/20756
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发表时间：
2020-12-15
影响因子：
7.4
通讯作者：
Househ M
中科院分区：
文献类型：
--
作者：
Abd-Alrazaq A;Alajlani M;Alhuwail D;Schneider J;Al-Kuwari S;Shah Z;Hamdi M;Househ M
2019年12月，新冠肺炎在武汉爆发，中国，导致国内和国际医疗、商业、教育、交通以及我们日常生活几乎方方面面的中断。人工智能(AI)在新冠肺炎大流行期间得到了利用；然而，人们对其用于支持公共卫生努力知之甚少。正如文献报道的那样，这次范围审查旨在探索人工智能技术在新冠肺炎大流行期间是如何被使用的。因此，这是第一次描述和总结已确定的人工智能技术的特征和用于其开发和验证的数据集。范围审查是按照PRISMA-SCR(系统审查的首选报告项目和范围审查的荟萃分析扩展)的指导方针进行的。我们检索了2020年4月10日至12日期间最常用的电子数据库(如MEDLINE、EMBASE和心理信息数据库)。这些术语是根据目标干预(即AI)和目标疾病(即新冠肺炎)选择的。两位评价者独立进行研究选择和数据提取。采用叙事法对提取的数据进行综合。在检索到的435篇研究中，我们考虑了82篇研究。人工智能最常见的用途是基于各种指标诊断新冠肺炎病例。人工智能还被用于药物和疫苗的发现或再利用，以及评估它们的安全性。此外，纳入的研究使用人工智能来预测新冠肺炎的流行发展，并预测其潜在的宿主和宿主。研究人员将人工智能用于与患者结局相关的任务，如评估新冠肺炎的严重程度，预测死亡风险及其相关因素，以及住院时间。人工智能被用于信息人口学，以提高使用水、卫生设施和个人卫生的意识。最突出的人工智能技术是卷积神经网络，其次是支持向量机。纳入的研究表明，人工智能具有对抗新冠肺炎的潜力。然而，许多建议的方法还没有在临床上被接受。因此，最有回报的研究将是在新冠肺炎之外有望实现价值的方法。需要做出更多努力来制定关于人工智能研究的标准化报告协议或指南。
In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.
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影响因子：
4.6
作者：
Lopez-Rincon A;Tonda A;Mendoza-Maldonado L;Mulders DGJC;Molenkamp R;Perez-Romero CA;Claassen E;Garssen J;Kraneveld AD
通讯作者：
Kraneveld AD
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7.4
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Contreras I;Vehi J
通讯作者：
Vehi J
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Demirci, Muserref Duygu Sacar;Adan, Aysun
通讯作者：
Adan, Aysun
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4.4
作者：
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通讯作者：
Mpesiana, Tzani A.
影响因子：
3.6
作者：
Anh-Tien Ton;Gentile, Francesco;Cherkasov, Artem
通讯作者：
Cherkasov, Artem