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How Explainable Artificial Intelligence Can Increase or Decrease Clinicians' Trust in AI Applications in Health Care:

Rikard Rosenbacke1, Åsa Melhus2, Martin McKee3

  • 1Centre for Corporate Governance, Department of Accounting, Copenhagen Business School, Frederiksberg, Denmark.

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Summary
This summary is machine-generated.

Explainable AI (XAI) can enhance clinicians' trust in artificial intelligence (AI) decision-making, but only when explanations are clear and relevant. Poorly designed explanations may reduce trust, highlighting the need for balanced AI integration in healthcare.

Keywords:
XAIaffect-based measuresclinical decision-makingclinical informaticsclinical useclinician trustcognitive measuresexplainable artificial intelligencetrustworthy AI

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Human-Computer Interaction

Background:

  • Artificial intelligence (AI) offers significant potential in clinical practice, but its
  • black box
  • nature can hinder clinician adoption.
  • Explainable AI (XAI) aims to build trust by providing insights into AI decision-making processes.
  • Ensuring appropriate clinician trust is crucial for effective AI integration, avoiding both over-reliance and skepticism.

Purpose of the Study:

  • To systematically review and synthesize empirical evidence on how Explainable AI (XAI) impacts clinician trust in AI-driven clinical decision-making.
  • To understand the nuances of trust modulation through AI explanations in healthcare settings.
  • To identify factors influencing the effectiveness of XAI in fostering appropriate levels of clinician trust.

Main Methods:

  • Systematic review conducted following PRISMA guidelines, searching PubMed and Web of Science.
  • Inclusion criteria focused on empirical studies measuring XAI's impact on clinician trust (cognitive or affect-based measures).
  • 10 studies were included out of 778 screened articles, with risk of bias assessed for each.

Main Results:

  • The majority of studies (5/10) found that XAI increased clinician trust compared to standard AI, particularly with clear, concise, and clinically relevant explanations.
  • Three studies reported no significant effect of XAI on trust, indicating explanations do not automatically improve it.
  • Two studies showed XAI could either enhance or diminish trust based on explanation complexity and coherence, emphasizing the critical role of explanation quality.

Conclusions:

  • The quality and clarity of explanations significantly modulate clinician trust in AI, with complex or contradictory explanations potentially undermining it.
  • Achieving an appropriate balance of trust is essential, preventing both blind faith and undue skepticism towards AI recommendations.
  • Further research is needed to refine trust measures and develop strategies for optimal AI integration in clinical practice.