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

Formative Evaluation of Digital Health Interventions (2491) JMIR Theme Issue: COVID-19 Special Issue (2461) Early Results from COVID-19 Studies (309) Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Outbreak and Pandemic Preparedness and Management (1743) Artificial Intelligence (1421) mHealth in a Clinical Setting (624) Design and Usability of Clinical Software and EHRs (271) Human Factors and Usability Case Studies (809) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Infectious Diseases (non-STD/STI) (1592)

Published on in Vol 5, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27992, first published .
Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis

Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis

Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis

Authors of this article:

Ahmed Abdulaal1 Author Orcid Image ;   Aatish Patel1 Author Orcid Image ;   Ahmed Al-Hindawi1 Author Orcid Image ;   Esmita Charani2 Author Orcid Image ;   Saleh A Alqahtani3, 4 Author Orcid Image ;   Gary W Davies1 Author Orcid Image ;   Nabeela Mughal1, 2, 5 Author Orcid Image ;   Luke Stephen Prockter Moore1, 2, 5 Author Orcid Image
Article Authors Cited by (8) Tweetations (8) Metrics

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  1. Alrujaib M, Bakheet D. What Is the Value of Artificial Intelligence in Radiology?. Innovations in Surgery and Interventional Medicine 2021;1(2):23 View
  2. Peng H, Hu C, Deng W, Huang L, Zhang Y, Luo B, Wang X, Long X, Huang X. Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model. BMC Pulmonary Medicine 2022;22(1) View
  3. Casano N, Santini S, Vittorini P, Sinatti G, Carducci P, Mastroianni C, Ciardi M, Pasculli P, Petrucci E, Marinangeli F, Balsano C. Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients. Journal of Integrative Bioinformatics 2023;20(2) View
  4. Liaw W, Ramos Silva Y, Soltero E, Krist A, Stotts A. An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study. JMIR AI 2023;2:e45032 View
  5. Okeibunor J, Jaca A, Iwu-Jaja C, Idemili-Aronu N, Ba H, Zantsi Z, Ndlambe A, Mavundza E, Muneene D, Wiysonge C, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Frontiers in Public Health 2023;11 View
  6. Wang L, Zhang Z, Wang D, Cao W, Zhou X, Zhang P, Liu J, Fan X, Tian F. Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review. Frontiers in Computer Science 2023;5 View
  7. Evans R, Bryant L, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. International Journal of Medical Informatics 2024;183:105342 View
  8. Perivolaris A, Adams-McGavin C, Madan Y, Kishibe T, Antoniou T, Mamdani M, Jung J. Quality of interaction between clinicians and artificial intelligence systems. A systematic review. Future Healthcare Journal 2024;11(3):100172 View