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Mapping Trust in Nurses with Dimensions of Trustworthy Artificial Intelligence: A Scoping Review.
Ronquillo, Charlene E; Booth, Richard G; Adzo Vittor, Winnifred; Mendoza, Isabella; Wood, Natasha; Gomes van Berlo, Olivia; Chan, Ryan; Recsky, Chantelle.
Afiliación
  • Ronquillo CE; School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
  • Booth RG; Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Adzo Vittor W; School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
  • Mendoza I; School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
  • Wood N; Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Gomes van Berlo O; Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Chan R; Arthur Labatt Family School of Nursing, Western University, London, Canada.
  • Recsky C; School of Nursing, University of British Columbia Okanagan, Kelowna, Canada.
Stud Health Technol Inform ; 315: 717-718, 2024 Jul 24.
Article en En | MEDLINE | ID: mdl-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.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Confianza Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Canadá

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Confianza Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Canadá