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

Letters to the Editor (86) Artificial Intelligence (2251) AI Applications in Public Health (33) Machine Learning (2168) Theme Issue: Medical Informatics and COVID-19 (125) JMIR Theme Issue: COVID-19 Special Issue (2559) Longitudinal and Cohort Studies in Public Health (426) Collateral Damage of the Pandemic: Excess deaths due to disruptions in healthcare and lockdowns (18) Intensive Care Unit (ICU) (162) Early Results from COVID-19 Studies (369)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82112, first published .
Authors’ Reply to: Significant Risks and Responsibilities in the Clinical Use of AI Predictive Models. Comment on: “AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients”

Authors’ Reply to: Significant Risks and Responsibilities in the Clinical Use of AI Predictive Models. Comment on: “AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients”

Authors’ Reply to: Significant Risks and Responsibilities in the Clinical Use of AI Predictive Models. Comment on: “AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients”

Authors of this article:

Jose Manuel Ruiz Giardin1, 2 Author Orcid Image ;   Óscar Garnica Alcázar3 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Tenchev P, Naseva E, Yancheva N. Factors Associated with Transfer to Intensive Care in Younger Adults Hospitalized with COVID-19. Germs 2026;16(1):4 View