Trust in AI applications and intention to use them in cardiac care among cardiologists in the UK: A Structural Equation Modeling Approach

Abstract

Background. The widespread use of Artificial Intelligence (AI)-driven applications among consultant cardiologists remains relatively low due to trust issues and perceived threat to professional autonomy, patient safety, and legal liability of misdiagnoses. There is a paucity of empirical research investigating the relationships between trust in AI applications and an intention to use (AI-Use) them among cardiologists. To address this gap, we surveyed a sample of cardiologists to examine the determinants of trust in AI and trust’s effects on AI-Use based on the organisational trust model. Methods. We conducted a cross-sectional survey of consultant cardiologists (n = 61) in the UK. Given the small sample size, we used a partial least square structural equation model (SEM) analysis approach to assess the measurement and structural models. We utilized factor loadings and weights for the measurement model assessment and coefficients, the redundancy indices, and goodness of fit (GoF) for the structural model assessment. We also undertook a content analysis of open-text responses around perceived risks, enablers, and barriers to AI use in cardiac care. We performed analyses in the R programme. Results. The GoF of the final SEM model was 63%, showcasing a substantial improvement over the original model (GoF=51%). The final model encompassed all latent constructs from the original model and explained 70% of the variance in trust and 37% in AI use. The AI application ability (accuracy and reliability) significantly influenced trust (β=0.55, p<.001), while lower benevolence correlated with decreased trust (β=0.19, p<.05). Trust in AI emerged as the sole significant contributor to AI-Use (β=0.48, p<.001), indicating higher trust associated with increased future use. Participants perceived diagnosis accuracy as a prominent theme, mentioned 20 times about AI risk and frequently cited as both an enabler (n=39 times) and a barrier (n=29 times). Conclusions. The enhanced GoF in the final model indicates an improved final SEM model compared with the original SEM model. Addressing diagnosis accuracy concerns and building trust in AI systems is crucial to facilitate increased AI adoption among cardiologists and seamless integration into cardiac care.