Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review
Open accessAbstract
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.
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Real-world adoption of AI in healthcare has been slow because clinicians are wary of opaque algorithms that they cannot verify.
“However, even with the significant promise real-world adoption has been slow because clinicians are wary of opaque algorithms that they cannot verify (Tun et al., 2025).”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
The Results (Insights Into Health Care Workers’ Trust in AI; no page available) identify black-box algorithms and difficult-to-interpret recommendations as barriers to clinicians’ trust. The Introduction links trust to successful clinical integration, but the paper does not establish that real-world adoption has been slow specifically because clinicians cannot verify opaque algorithms; it identifies several other barriers as well. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCommon barriers to trust in AI-CDSS include algorithmic opacity and lack of user training, whereas enabling factors include transparent design and proven reliability.
“Common barriers include algorithmic opacity and lack of user training, whereas enabling factors include transparent design and proven reliability.”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
Lack of transparency is a major barrier to AI-CDS adoption.
“leading studies have repeatedly identified that lack of transparency is a major barrier to AI-CDS adoption (Tun et al., 2025; Amann et al., 2022).”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
Usability and workflow integration are critical for adoption of clinical AI.
“This respects the finding that usability and workflow integration are key enablers for AI adoption (Tun et al., 2025).”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
Tun et al. (2025) summarize eight themes crucial to trust in AI-CDSS: system transparency, user training, usability and workflow integration, clinical reliability and consistency, validation across contexts, ethical and fairness concerns, patient-centeredness, and customization and clinician control.
“In one systematic review of 27 studies, Tun et al., (2025) summarize eight themes crucial to trust in AI-CDSS: (1) system transparency, (2) user training, (3) usability and workflow integration, (4) clinical reliability and consistency, (5) validation across contexts, (6) ethical and fairness concerns, (7) patient-centeredness, and (8) customization and clinician control (Tun et al., 2025).”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
System transparency-being able to understand how outputs are derived-is essential for user trust.
“Tun et al., (2025) emphasize that system transparency, being able to understand how outputs are derived is essential for user trust (Tun et al., 2025).”
From An auditable and source-verified framework for clinical AI decision support: integrating retrieval-augmented generation with data provenance (2026) · cites this paper as Tun et al., 2025
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“The importance of human-centric design cannot be oversta-ted in fostering trust in AI-CDSS [50,51,52,53,54, 55,56 ].”
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Cited by
Sorted by citations
Alu, F. F., & Oluwadare, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1737532 · Cited by 13 · see citing works
“This respects the finding that usability and workflow integration are key enablers for AI adoption (Tun et al., 2025).”
InfluentialZhu, Y., Sui, D., Wang, Z., Hu, X., Gu, L., Qi, Y., Wu, T., Wang, L., Wei, Y., Tang, W., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791458 · Cited by 3 · see citing works
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Disha, J. F., Dehan, M. F. Z., & Anzum, K. M. T. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70487
- Authors
- Tun, H. M., Rahman, H. A., Naing, L., & Malik, O. A.
- Affiliations
- Universiti Brunei Darussalam (BN)
- Corresponding author
- Tun, Hein Minn
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-07-29
- Volume
- 27
- Pages
- e69678-e69678
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC12440830
- OpenAlex
- W4406892302
- Cited by
- 210
- References
- 17
- Citations by year
- 2026: 191 · 2025: 19
- FWCI
- 12.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- trust in AI, clinical decision support systems, healthcare workers, AI transparency, AI usability, clinical reliability, algorithmic opacity, human-centered design, AI ethics, medicolegal liability, clinical workflow integration, decision-making autonomy, trust-building strategies, AI adoption in healthcare, personalized medicine, patient-centered care
- MeSH terms
- Artificial Intelligence, Health Personnel, Humans, Decision Support Systems, Clinical, Trust
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