Understanding Trust as a Mediating Mechanism in AI-Based Medical Tools and Clinical Decision Processes

Abstract

The growing integration of artificial intelligence (AI) in healthcare has transformed clinical decision-making. Nevertheless, doctors’ engagement with AI-based tools remains hindered, particularly due to trust-related concerns. The purpose of this study is to examine the factors influencing doctors’ engagement with AI-based medical tools, with a focus on the mediating role of trust within the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from 197 doctors in Selangor and Kuala Lumpur through a structured survey and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4 software. The findings reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence trust in AI tools, which in turn strongly predict AI-based clinical decision-making. Mediation analysis confirms that trust fully mediates the relationships between UTAUT constructs and AI engagement. Hence, trust is a critical mechanism linking user perceptions to behavioural outcomes in clinical contexts. The findings implied the need for transparent AI systems, targeted training and institutional support. Healthcare institutions should focus on clear governance to strengthen trust and facilitate effective human-AI integration.