Medical Big Data and Wearable Internet of Things Healthcare Systems in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients

Abstract

Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. 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. Results and Discussion Artificial intelligence-enabled wearable medical devices for preliminary disease detection and monitoring and physiochemical alterations assist in medical diagnosis, assessing infection levels and subsequent therapeutic decision through artificial intelligence-driven biosensors. (Jaleel et al., 2020) Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare by enabling data analytics-based smart medical services (Lăzăroiu et al., 2021) in evidence-based decision making, remote monitoring, disease prevention and diagnoses, and risk factor identification.