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Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.大流行冠状病毒病(COVID-19)中的流行病学挑战:人工智能的作用
Wiley Interdiscip Rev Data Min Knowl Discov. 2022 Jul-Aug;12(4):e1462. doi: 10.1002/widm.1462. Epub 2022 Jun 28.
2
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.使用X射线图像和深度卷积神经网络自动检测冠状病毒病(COVID-19)。
Pattern Anal Appl. 2021;24(3):1207-1220. doi: 10.1007/s10044-021-00984-y. Epub 2021 May 9.
3
Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.利用感染大小感知分类进行大规模筛选,以区分 COVID-19 和社区获得性肺炎。
Phys Med Biol. 2021 Mar 17;66(6):065031. doi: 10.1088/1361-6560/abe838.
4
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.深度学习利用 CT 图像准确诊断新型冠状病毒(COVID-19)。
IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2775-2780. doi: 10.1109/TCBB.2021.3065361. Epub 2021 Dec 8.
5
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.
6
Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.用于区分新型冠状病毒肺炎和流感肺炎的深度学习
Ann Transl Med. 2021 Jan;9(2):111. doi: 10.21037/atm-20-5328.
7
Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.基于置信度感知异常检测的胸部 X 射线病毒性肺炎筛查。
IEEE Trans Med Imaging. 2021 Mar;40(3):879-890. doi: 10.1109/TMI.2020.3040950. Epub 2021 Mar 2.
8
DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.DeepCOVID-XR:一种人工智能算法,可在美国大型临床数据集上进行训练和测试,用于检测胸部 X 光片上的 COVID-19。
Radiology. 2021 Apr;299(1):E167-E176. doi: 10.1148/radiol.2020203511. Epub 2020 Nov 24.
9
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.用于新冠病毒疾病筛查的人工智能辅助CT影像分析:构建与部署医学人工智能系统
Appl Soft Comput. 2021 Jan;98:106897. doi: 10.1016/j.asoc.2020.106897. Epub 2020 Nov 10.
10
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.COVID-Net:一种针对胸部 X 光图像中 COVID-19 病例检测的定制化深度卷积神经网络设计。
Sci Rep. 2020 Nov 11;10(1):19549. doi: 10.1038/s41598-020-76550-z.

SARS-CoV-2:人工智能是否经受住了时间的考验。

SARS-CoV-2: Has artificial intelligence stood the test of time.

机构信息

Medical College, Aga Khan University, Stadium Road, Karachi, Pakistan.

Medlcal College, Islamabad Medical and Dental College, Main Murree Road, Islamabad, Pakistan.

出版信息

Chin Med J (Engl). 2022 Aug 5;135(15):1792-1802. doi: 10.1097/CM9.0000000000002058.

DOI:10.1097/CM9.0000000000002058
PMID:36195992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9521771/
Abstract

Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the "smart lockdown" strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government-public relations.

摘要

人工智能(AI)在生活的各个领域已经被证明是一个具有变革意义的创新,包括医学。1976 年,Gunn 博士引入 AI 来准确诊断急性腹痛并列出潜在的鉴别诊断,从那时起,AI 已经取得了长足的进步。特别是,人工智能通过使用机器学习算法,在放射学诊断中具有良好的敏感性和特异性。在 2019 年冠状病毒病大流行期间,人工智能已经证明不仅仅是一种工具,可以帮助医疗工作者在大流行期间做出决策并限制医患接触。它还指导政府和主要政策制定者制定和实施法律,如封锁和旅行限制,以遏制这种病毒性疾病的传播。这是通过使用社交媒体来绘制严重急性呼吸系统综合症冠状病毒 2 热点图实现的,为全球采用的“智能封锁”策略奠定了基础。然而,这些好处可能伴随着对隐私和未经同意的监控的担忧,这需要当局发展真诚和道德的政府-公众关系。