Exploring trust factors in AI-healthcare integration: a rapid review

Abstract

This rapid review explores how artificial intelligence (AI) is integrated into healthcare and examines the factors influencing trust between users and AI systems. By systematically identifying trust-related determinants, this review provides actionable insights to support effective AI adoption in clinical settings. A comprehensive search of MEDLINE (Ovid), Embase (Ovid), and CINAHL (Ebsco) using keywords related to AI, healthcare, and trust yielded 872 unique citations, of which 40 studies met the inclusion criteria after screening. Three core themes were identified. AI literacy highlights the importance of user understanding of AI inputs, processes, and outputs in fostering trust among patients and clinicians. AI psychology reflects demographic and experiential influences on trust, such as age, gender, and prior AI exposure. AI utility emphasizes perceived usefulness, system efficiency, and integration within clinical workflows. Additional considerations include anthropomorphism, privacy and security concerns, and trust-repair mechanisms following system errors, particularly in high-risk clinical contexts. Overall, this review advances the understanding of trustworthy AI in healthcare and offers guidance for future implementation strategies and policy development.

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