人工智能是克服COVID-19大流行带来的重大健康问题的有力工具

V. Jokanović, M. Živković, S. Živković
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摘要

介绍。本文旨在探讨应用人工智能诊断SARS - Cov-2和预测潜在突发事件发展的现代方法。方法。检索了2020年最常用的电子数据库Scopus和Medline。采用叙述方法对提取的数据进行综合。结果。本文综述了人工智能在临床试验中对病毒诊断和预后的重要作用。它可以使医院在治疗SARS - Cov-2和预测可能的死亡率期间更合理地使用资源,例如呼吸器。所获得的结果来自对120篇论文和研究的分析,这些论文和研究是以电子方式从Scopus和Pub Med line上发表的论文中提取的。最常用的人工智能技术是卷积神经网络和机器学习。结论。纳入的研究表明,人工智能可以显着改善SARS Cov-2的治疗,尽管许多提出的方法尚未被临床接受。此外,需要更多的努力来制定将人工智能应用于常规临床实践的标准化报告协议或指南。该技术适用于对当前患者的快速准确诊断、预测和监测以及对未来患者疾病发展的预后。
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Artificial intelligence as a powerful tool in overcoming substantial health problems of the COVID-19 pandemic
Introduction. This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods. The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results. In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators, in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions. Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. In addition, more effort is needed to develop standardized reporting protocols or guidelines on applying artificial intelligence into conventional clinical practice. This technology is suitable for fast and accurate diagnosis, prediction and monitoring of current patients and prognosis of disease development in future patients.
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