Exploring the impact of patients' risk-benefit and knowledge perceptions on trust and intention to use AI-based medical imaging tools in radiology

The integration of artificial intelligence (AI) with medical imaging tools has enabled faster and more accurate diagnostic processes, transforming radiology into a more precise, efficient, and data-driven medical discipline. However, the successful implementation of AI-based medical imaging tools in emotionally sensitive, life-critical domains such as radiology depends heavily on public trust and acceptance. This study examines how value perceptions and trust shape behavioral intentions to adopt AI-based tools in radiology by extending Esmaeilzadeh’s Value-Based Model, which is conceptually aligned with Privacy Calculus Theory. To enhance the model’s explanatory power, additional variables were incorporated, including perceived knowledge as a predictor and trust as a mediating factor. A cross-sectional online survey (N = 961) was conducted, and data was analyzed through structural equation modeling. The findings indicate that perceived risk, perceived benefit, and perceived knowledge significantly influence trust perception. Importantly, trust served as a key mediating variable, partially mediating the effects of these factors on the intention to use AI-based medical imaging tools. The inclusion of trust increased the model’s explanatory power from R 2 = 0.68 to R 2 = 0.74. Multigroup analysis based on gender, age, and education level revealed significant differences in certain pathways; however, the effect sizes were small. These findings highlight the importance of developing inclusive and targeted strategies that address both technical and emotional concerns, enhance perceived benefits, foster public trust, and strengthen the intention to use AI-based tools in radiology. • Extends Esmaeilzadeh’s Value-Based Model to AI adoption in radiology. • Identifies trust as the key mediator linking risk, benefit, and AI knowledge. • Highlights user trust as a critical perception shaping public adoption of medical AI. • Shows perceived benefit outweighs perceived risk in high-stakes radiology. • Demonstrates AI literacy shapes trust through benefit-oriented evaluations.