Mapping Trust in Nurses with Dimensions of Trustworthy Artificial Intelligence: A Scoping Review.

Journal: Studies in health technology and informatics
PMID: 39049396

Abstract

This scoping review examines the concept of trust in nursing and its potential application in developing trustworthy Artificial Intelligence (AI) for healthcare. Recognizing nurses as highly trusted professionals, the study explores how attributes contributing to trust in nursing can inform AI development. Following the Joanna Briggs Institute framework, the review synthesizes literature on patients' perceptions of nurses' trustworthiness and compares these with desired qualities in trustworthy AI. Preliminary findings suggest that nursing's trust-inducing actions could offer valuable insights for implementing trust-enhancing features in AI. This approach aims to bring innovative insights into the nature of trust and contribute to creative solutions to develop trustworthy AI in healthcare. By aligning AI development with principles of trust observed in nursing, the review proposes novel strategies for creating more ethical and accepted AI systems in healthcare settings.

Authors

  • Charlene E Ronquillo
    School of Nursing, University of British Columbia Okanagan, Kelowna, BC, Canada. Electronic address: charlene.ronquillo@ubc.ca.
  • Richard G Booth
    Arthur Labatt Family School of Nursing, Western University, London, ON, Canada.
  • Winnifred Adzo Vittor
    School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
  • Isabella Mendoza
    School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
  • Natasha Wood
    Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Olivia Gomes van Berlo
    Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Ryan Chan
    Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA.
  • Chantelle Recsky
    School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.

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