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Trust in Artificial Intelligence-Based Clinical Decision Support Systems Among Health Care Workers: Systematic

Hein Minn Tun1,2, Hanif Abdul Rahman1,2, Lin Naing1

  • 1PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Core Residential, Tower 4, Room 201A, UBDCorp, Jalan Tungku Link, Bandar Seri Begawan, BE1410, Brunei Darussalam, 673 7428942.

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|August 7, 2025
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Summary

Building trust in artificial intelligence-based clinical decision support systems (AI-CDSSs) requires transparency, usability, and reliability. These factors are crucial for successful integration of AI-CDSSs into healthcare practice.

Keywords:
PRISMAdecision support systemshealth care workerstrust in artificial intelligence

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Area of Science:

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

Background:

  • Artificial intelligence-based clinical decision support systems (AI-CDSSs) offer significant potential for personalized medicine and healthcare efficiency.
  • However, a critical barrier to the widespread adoption of AI-CDSSs is the lack of trust among healthcare workers.
  • Existing research has not yet synthesized actionable recommendations for developing trustworthy AI-CDSSs.

Purpose of the Study:

  • To systematically review and synthesize the key factors influencing healthcare workers' trust in AI-CDSSs.
  • To provide evidence-based, actionable recommendations for enhancing trust in these systems.

Main Methods:

  • A systematic review of studies published between January 2020 and November 2024 from PubMed, Scopus, and Google Scholar.
  • Inclusion criteria focused on studies examining healthcare workers' perceptions, experiences, and trust in AI-CDSSs.
  • Adherence to PRISMA 2020 guidelines and critical appraisal using the CASP tool ensured methodological rigor.

Main Results:

  • Twenty-seven studies involving diverse healthcare workers, primarily in inpatient settings, were included.
  • Eight key themes emerged: System Transparency, Training and Familiarity, System Usability, Clinical Reliability, Credibility and Validation, Ethical Consideration, Human-Centric Design, and Customization and Control.
  • Key enablers of trust included transparency, usability, and clinical reliability, while algorithmic opacity and insufficient training were identified as barriers.

Conclusions:

  • Fostering healthcare worker trust in AI-CDSSs necessitates explainable AI, comprehensive training, stakeholder engagement, and human-centered design.
  • Future research should address heterogeneity in study designs and explore trust across diverse demographics and healthcare settings.
  • This review provides a foundational synthesis of trust factors, guiding future AI-CDSS development and implementation.