This paper is in the following e-collection/theme issue:

Early Results from COVID-19 Studies (342) Non-randomized Protocols and Methods (ehealth) (623) JMIR Theme Issue: COVID-19 Special Issue (2502) Artificial Intelligence (1786) Clinical Information and Decision Making (1696) Wearable Devices and Sensors (1017)

Published on in Vol 6, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36933, first published .
Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study

Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study

Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study

Authors of this article:

Evangelos Logaras1 Author Orcid Image ;   Antonis Billis1 Author Orcid Image ;   Ilias Kyparissidis Kokkinidis1 Author Orcid Image ;   Smaranda Nafsika Ketseridou1 Author Orcid Image ;   Alexios Fourlis2 Author Orcid Image ;   Aristotelis Tzotzis3 Author Orcid Image ;   Konstantinos Imprialos3 Author Orcid Image ;   Michael Doumas3 Author Orcid Image ;   Panagiotis Bamidis1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Kuo K, Chang C. A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions. BMC Medical Informatics and Decision Making 2025;25(1) View