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

Abstract

Background
Artificial intelligence–based clinical decision support systems (AI-CDSSs) have enhanced personalized medicine and improved the efficiency of health care workers. Despite these opportunities, trust in these tools remains a critical factor for their successful integration into practice. Existing research lacks synthesized insights and actionable recommendations to guide the development of AI-CDSSs that foster trust among health care workers.


Objective
This systematic review aims to identify and synthesize key factors that influence health care workers’ trust in AI-CDSSs and to provide actionable recommendations for enhancing their trust in these systems.


Methods
We conducted a systematic review of published studies from January 2020 to November 2024, retrieved from PubMed, Scopus, and Google Scholar. Inclusion criteria focused on studies that examined health care workers’ perceptions, experiences, and trust in AI-CDSSs. Studies in non–English languages and those unrelated to health care settings were excluded. Two independent reviewers followed the Cochrane Collaboration Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Analysis was conducted using a developed data charter. The Critical Appraisal Skills Programme tool was applied to assess the quality of the included studies and to evaluate the risk of bias, ensuring a rigorous and systematic review process.


Results
A total of 27 studies met the inclusion criteria, involving diverse health care workers, predominantly in hospitalized settings. Qualitative methods were the most common (n=16, 59%), with sample sizes ranging from small focus groups to cohorts of over 1000 participants. Eight key themes emerged as pivotal in improving health care workers’ trust in AI-CDSSs: (1) System Transparency, emphasizing the need for clear and interpretable AI; (2) Training and Familiarity, highlighting the importance of knowledge sharing and user education; (3) System Usability, focusing on effective integration into clinical workflows; (4) Clinical Reliability, addressing the consistency and accuracy of system performance; (5) Credibility and Validation, referring to how well the system performs across diverse clinical contexts; (6) Ethical Consideration, examining medicolegal liability, fairness, and adherence to ethical standards;(7) Human Centric Design, pioritizing patient centered approaches; (8) Customization and Control, highlighting the need to tailor tools to specific clinical needs while preserving health care providers’ decision-making autonomy. Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers’ trust in AI-CDSS tools were transparency, usability, and clinical reliability.


Conclusions
The findings highlight the need for explainable AI models, comprehensive training, stakeholder involvement, and human-centered design to foster health care workers’ trust in AI-CDSSs. Although the heterogeneity of study designs and lack of specific data limit further analysis, this review bridges existing gaps by identifying key themes that support trust in AI-CDSSs. It also recommends that future research include diverse demographics, cross-cultural perspectives, and contextual differences in trust across various health care professions.