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引用次数: 0
摘要
最近,人工智能和机器学习取得了突破性进展,研究人员和科学家可以利用基于计算的模型将关联数据转化为有用信息,从而帮助疾病诊断、检查和病毒遏制。在本文中,我们广泛研究了人工智能和机器学习在 COVID-19 大流行开始近四年后的高效应对中发挥的作用。在这方面,我们研究了多个学科的学术和研究团体开展的大量重要研究,以及人工智能算法的实际应用,这些算法为调查不同的 COVID-19 决策场景提出了潜在的解决方案。我们确定了人工智能和机器学习可对这一背景产生影响的众多领域,包括诊断(使用胸部 X 光成像和 CT 成像)、严重程度、跟踪、治疗和制药业。此外,我们还分析了这一困境的局限性、限制和危害。
Artificial intelligence and machine learning responses to COVID-19 related inquiries.
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
期刊介绍:
The Journal of Medical Engineering & Technology is an international, independent, multidisciplinary, bimonthly journal promoting an understanding of the physiological processes underlying disease processes and the appropriate application of technology. Features include authoritative review papers, the reporting of original research, and evaluation reports on new and existing techniques and devices. Each issue of the journal contains a comprehensive information service which provides news relevant to the world of medical technology, details of new products, book reviews, and selected contents of related journals.