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

JMIR Theme Issue: COVID-19 Special Issue (2452) Theme Issue 2022: Chatbots and COVID-19 (42) Clinical Information and Decision Making (1407) Machine Learning (1465) Artificial Intelligence (1371) New Methods (944)

Published on in Vol 25 (2023)

This is a member publication of University of Cambridge (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44804, first published .
Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study

Authors of this article:

Jing Han1 Author Orcid Image ;   Marco Montagna2 Author Orcid Image ;   Andreas Grammenos1 Author Orcid Image ;   Tong Xia1 Author Orcid Image ;   Erika Bondareva1 Author Orcid Image ;   Chloë Siegele-Brown3 Author Orcid Image ;   Jagmohan Chauhan3 Author Orcid Image ;   Ting Dang1 Author Orcid Image ;   Dimitris Spathis1 Author Orcid Image ;   R Andres Floto4 Author Orcid Image ;   Pietro Cicuta5 Author Orcid Image ;   Cecilia Mascolo1 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

Journals

  1. Arnold V, Purnat T, Marten R, Pattison A, Gouda H. Chatbots and COVID-19: Taking Stock of the Lessons Learned. Journal of Medical Internet Research 2024;26:e54840 View
  2. Drissi N, El-Kassabi H, Serhani M. A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research. Decision Analytics Journal 2024;13:100523 View