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

Design and Usability of Clinical Software and EHRs (295) Clinical Information and Decision Making (1612) Decision Support for Health Professionals (1326) Safety and Error Prevention in Health (223) Artificial Intelligence (1676) Intensive Care Unit (ICU) (135)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39114, first published .
Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study

Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study

Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study

Authors of this article:

Siri L van der Meijden1, 2, 3 Author Orcid Image ;   Anne A H de Hond2, 4 Author Orcid Image ;   Patrick J Thoral5 Author Orcid Image ;   Ewout W Steyerberg4 Author Orcid Image ;   Ilse M J Kant2, 4 Author Orcid Image ;   Giovanni Cinà6, 7, 8 Author Orcid Image ;   M Sesmu Arbous1 Author Orcid Image
Article Authors Cited by (12) Tweetations (7) Metrics

Journals

  1. Kang C, Yoon J. Current challenges in adopting machine learning to critical care and emergency medicine. Clinical and Experimental Emergency Medicine 2023;10(2):132 View
  2. Stasevych M, Zvarych V. Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review. Big Data and Cognitive Computing 2023;7(3):147 View
  3. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Perspectivas actuales sobre el uso de la inteligencia artificial en la seguridad del paciente crítico. Medicina Intensiva 2025;49(3):154 View
  4. McLennan S, Fiske A, Celi L. Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine. BMJ Health & Care Informatics 2024;31(1):e101052 View
  5. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Current perspectives on the use of artificial intelligence in critical patient safety. Medicina Intensiva (English Edition) 2025;49(3):154 View
  6. Rakers M, Mwale D, de Mare L, Chirambo L, Bierling B, Likumbo A, Langton J, Chavannes N, van Os H, Calis J, Dellimore K, Villalobos-Quesada M. Cautiously optimistic: paediatric critical care nurses’ perspectives on data-driven algorithms in low-resource settings—a human-centred design study in Malawi. BMC Global and Public Health 2024;2(1) View
  7. Düvel J, Lampe D, Kirchner M, Elkenkamp S, Cimiano P, Düsing C, Marchi H, Schmiegel S, Fuchs C, Claßen S, Meier K, Borgstedt R, Rehberg S, Greiner W. An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis. JMIR Human Factors 2025;12:e66699 View
  8. Tekin A, Herasevich S, Minteer S, Gajic O, Barwise A. Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections. Applied Clinical Informatics 2025;16(01):223 View
  9. Uluç K. Yoğun Bakım Ünitesinde Çalışan Doktorların Yapay Zeka İle İlgili Bakış Açıları. Acta Medica Nicomedia 2025;8(1):84 View
  10. Alves J, Azevedo R, Marques A, Encarnação R, Alves P. Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review. Nursing Reports 2025;15(4):126 View
  11. Mohseni Ahangar R, Nouri S, Navari S, Mohammadi Kiani B, Poursiahbidi P. Attitudes of Healthcare Professionals Toward Artificial Intelligence in Clinical Decision-Making: A Cross-Sectional Survey in Iran. InfoScience Trends 2025;2(3):25 View
  12. Popoff B, Cabon S, Cuggia M, Bouzillé G, Clavier T. Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study. JMIR Medical Informatics 2025;13:e63709 View