Trust in Artificial Intelligence in Cardiovascular Scenarios: An Analysis of Digital-Savvy Early Adopters and the AI Literacy Paradox

Background/Aims: Artificial intelligence (AI) is fundamentally transforming cardiovascular care, but patient trust remains a primary barrier to clinical integration. This study investigates the “AI Literacy Paradox” among digital-savvy early adopters to identify predictors of trust across varied clinical scenarios, ranging from routine monitoring to critical decisions. Methods: A cross-sectional survey was conducted with 254 technologically proficient participants (mean age: 39.7 years). Trust was evaluated across 11 cardiovascular scenarios involving medication management, emergency triage, and diagnostic conflicts. Statistical analyses included binary logistic regression to identify predictors, K-means clustering for patient segmentation, and Mann–Whitney U tests to evaluate algorithm aversion. Results: AI knowledge was a strong positive predictor of trust (odds ratio [OR]: 3.522). However, holders of graduate degrees exhibited “informed skepticism,” with 66% lower odds of trust (OR: 0.340), illustrating the AI Literacy Paradox. Significant trust gaps were also identified for women (OR: 0.610) and patients with chronic illnesses (OR: 0.563). Participants demonstrated clear algorithm aversion in high-stakes scenarios, with over 92% preferring physician-led medication management and 87.4% prioritizing physician judgment over AI in diagnostic conflicts. Four distinct user segments were identified, with “Older Educated Female Neutrals” (53.5%) forming the largest group. Conclusions: Trust in AI in cardiovascular scenarios is a multidimensional, segment-specific construct. Higher education levels often lead to critical scrutiny rather than blind acceptance. To ensure successful adoption, AI integration must prioritize hybrid “physician-in-the-loop” models that preserve the human-centric nature of the therapeutic alliance while addressing the specific privacy and safety concerns of diverse user segments.