Artificial Intelligence-Powered Diagnostic Tools, Networked Medical Devices, and Cyber-Physical Healthcare Systems in Assessing and Treating Patients with COVID-19 Symptoms

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI:10.22381/ajmr8220217

Helen Michalikova Katarina Frajtova Welch

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引用次数: 2

Abstract

Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

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人工智能驱动的诊断工具、联网医疗设备和网络物理医疗系统在评估和治疗COVID-19症状患者中的应用

人工智能驱动的诊断工具、联网医疗设备和网络物理医疗系统在评估和治疗COVID-19患者症状方面的经验证据在文献中几乎没有记录。(Tsikala Vafea et al.， 2020)医疗物联网需要部署来自可穿戴移动医疗保健和智能传感设备的健康数据，以及临床和诊断决策支持以及远程医疗保健系统中电子健康记录联网的应用程序。(Williams Samuel et al.， 2020) COVID-19检测和监测系统可以从支持人工智能的可穿戴医疗设备获取即时症状数据，通过使用机器学习算法识别潜在的COVID-19病例。研究设计、调查方法和材料访谈是在线进行的，数据采用人口普查局美国社区调查的五个变量(年龄、种族/民族、性别、教育程度和地理区域)加权，以可靠和准确地反映美国的人口构成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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American journal of medical research (New York, N.Y.)

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