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  3. Linking Explainability, Trust, And Use: A Framework For Clinical Decision Support.
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  3. Linking Explainability, Trust, And Use: A Framework For Clinical Decision Support.

Related Experiment Video

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Linking Explainability, Trust, and Use: A Framework for Clinical Decision Support.

Tom Strube1, Leoni Weltermann1, Jonas Weber2

  • 1Department Healthcare, Fraunhofer Institute for Software and Systems Engineering ISST, Dortmund, Germany.

Studies in Health Technology and Informatics
|May 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Clinician trust in artificial intelligence clinical decision support systems (AI-CDSS) is crucial for adoption. This study proposes a model connecting AI explainability and reliability to clinician trust and AI-CDSS use in healthcare.

Keywords:
Artificial IntelligenceClinical Decision SupportExplainabilityTrust

Related Experiment Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Human-Computer Interaction

Background:

  • Limited adoption of AI-based clinical decision support systems (AI-CDSS) in healthcare settings.
  • Insufficient clinician trust in AI models, particularly in high-risk medical scenarios, hinders AI-CDSS implementation.
  • Need for a framework to enhance trust and promote the use of AI-CDSS.

Purpose of the Study:

  • To develop an initial conceptual model that links AI explainability and reliability to clinician trust.
  • To investigate the relationship between clinician trust and the intention to use AI-CDSS.
  • To provide guidance for designing trustworthy AI-CDSS for healthcare.

Main Methods:

  • Conceptual modeling approach.
  • Literature review on AI trust, explainability, and reliability in healthcare.
  • Synthesis of existing theories and empirical evidence.

    • Main Results:

    • A conceptual model is proposed, illustrating the pathways from AI explainability and reliability to clinician trust.
    • The model posits that enhanced explainability and reliability positively influence clinician trust in AI-CDSS.
    • Clinician trust is identified as a key mediator for the intention to use AI-CDSS.

    Conclusions:

    • Explainability and reliability are critical factors for building clinician trust in AI-CDSS.
    • Trustworthy AI-CDSS design is essential for successful adoption and integration into clinical practice.
    • The conceptual model serves as a foundation for future empirical research and AI-CDSS development.