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

Safety and Error Prevention in Health (240) Clinical Information and Decision Making (1915) Artificial Intelligence (2129) Decision Support for Health Professionals (1539)

Published on in Vol 9, No 4 (2022): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38411, first published .
Clinicians’ Perceptions of an Artificial Intelligence–Based Blood Utilization Calculator: Qualitative Exploratory Study

Clinicians’ Perceptions of an Artificial Intelligence–Based Blood Utilization Calculator: Qualitative Exploratory Study

Clinicians’ Perceptions of an Artificial Intelligence–Based Blood Utilization Calculator: Qualitative Exploratory Study

Authors of this article:

Avishek Choudhury1 Author Orcid Image ;   Onur Asan2 Author Orcid Image ;   Joshua E Medow3 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. van der Meijden S, de Hond A, Thoral P, Steyerberg E, Kant I, Cinà G, Arbous M. Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study. JMIR Human Factors 2023;10:e39114 View
  2. Labarta J, Dimitri P, Keiser M, Koledova E, Rivera-Romero O. Evaluating the Usefulness and Ease of Use of a Next-Generation–Connected Drug Delivery Device for Growth Hormone Therapy: Qualitative Study of Health Care Professionals’ Perceptions. JMIR Human Factors 2023;10:e46893 View
  3. Lambert S, Madi M, Sopka S, Lenes A, Stange H, Buszello C, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine 2023;6(1) View
  4. Wosny M, Strasser L, Hastings J. Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review. JMIR Human Factors 2023;10:e50357 View
  5. Allen M, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G. Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC Primary Care 2024;25(1) View
  6. Bach T, Kristiansen J, Babic A, Jacovi A. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review. IEEE Access 2024;12:106385 View
  7. Al‐Riyami A, Gammon R, Seheult J, Arora S, Goel R. Artificial intelligence and transfusion education, research and practice: The view from the ISBT Clinical Transfusion Working Party. Vox Sanguinis 2025 View
  8. Tun H, Rahman H, Naing L, Malik O. Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research 2025;27:e69678 View
  9. An S, Teo K, McConnell M, Marshall J, Galloway C, Squirrell D. AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed. Progress in Retinal and Eye Research 2025;106:101352 View
  10. Villarreal J, Suh E, Forrester J, Jopling J, Dumas R. Current State of Artificial Intelligence and Trauma Video Review: Insights for Trauma Resuscitation. Current Trauma Reports 2025;11(1) View
  11. Parsons C, Zuiderwijk A, Orchard N, Oosterhoff J, de Reuver M. Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies. BMC Medical Informatics and Decision Making 2025;25(1) View
  12. Badawi M. Artificial intelligence in blood donor management: A narrative review. Vox Sanguinis 2025 View

Conference Proceedings

  1. Anugerah G, Hidayanto A. 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). What Factors Contribute to the Acceptance of Artificial Intelligence on Healthcare Sector: A Systematic Review View