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Artificial Intelligence in nursing: trustworthy or reliable?

Oliver Higgins1,2, Stephan K Chalup3, Rhonda L Wilson4

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Trustworthiness in clinical Artificial Intelligence (AI) requires evaluating its reliability and validity, not just trust. Reframing AI trustworthiness ensures safe and effective integration into healthcare practices.

Keywords:
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Area of Science:

  • Healthcare innovation
  • Clinical informatics
  • Artificial Intelligence in Medicine

Background:

  • Clinicians express concerns about trust and confidence as barriers to adopting Artificial Intelligence (AI) in clinical settings.
  • AI currently lacks the emotional capacity for the reciprocal nature of trust essential in human interactions.

Purpose of the Study:

  • To address the challenge of expecting human-like trust from AI systems.
  • To redefine AI trustworthiness by assessing its reliability and validity, akin to other clinical instruments.

Main Methods:

  • Conceptual analysis of trust in AI within clinical contexts.
  • Evaluation of AI performance based on reliability and validity metrics.

Main Results:

  • AI interventions should be assessed for competence, reliability, and validity, prioritizing quality and safety.
  • Nurses require treatment recommendations detailing AI prediction validity and confidence, with the final decision remaining with the clinician.
  • Future research should explore AI's role in care delivery and its impact on nursing practice.

Conclusions:

  • Focusing solely on trust, rather than reliability and validity, can lead to negative experiences for clinical users.
  • Successful AI implementation in nursing necessitates understanding the complexities of trust and credibility.