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Enhancing AI Clinical Decision Support Trust: Design Workshop Insights from General Practitioners.

R A D L M K Ranwala1, Andre Q Andrade1

  • 1University of South Australia.

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General Practitioners need more than just explainable AI (Artificial Intelligence) visualizations. Transparency in AI training data and alignment with clinical guidelines are key to building trust and acceptance of AI in healthcare.

Keywords:
AIAI-TrustCDSSXAI

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Area of Science:

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

Background:

  • Artificial Intelligence (AI) predictive models are being integrated into Clinical Decision Support Systems (CDSS).
  • Healthcare provider trust and acceptance remain significant barriers to the real-world implementation of AI-based CDSS.
  • Understanding these barriers is crucial for successful AI adoption in clinical practice.

Purpose of the Study:

  • To investigate factors influencing healthcare provider trust and acceptance of AI-based CDSS.
  • To explore user, model, and organizational factors affecting clinician trust and recommendation acceptance.
  • To identify actionable insights for designing AI systems that foster trust.

Main Methods:

  • A workshop was conducted with General Practitioners (GPs).
  • Discussions focused on user, model, and organizational factors impacting trust and acceptance of AI-CDSS.
  • Qualitative insights were gathered from clinician participants.

Main Results:

  • Explainability is vital, but current Explainable AI (XAI) visualizations offer limited value to non-technical clinicians.
  • Clinicians desire transparency in AI model training data (sources, elements).
  • Alignment of AI recommendations with existing clinical research and guidelines is essential for trust.

Conclusions:

  • Enhanced AI system design requires more than just technical explainability.
  • Transparency in data and alignment with clinical evidence are critical for clinician trust and acceptance of AI-CDSS.
  • Future AI-CDSS development should prioritize user-centered design focusing on trust-building elements.