Predictors of Trust and Engagement in Personalized Healthcare: A Study of AI-Driven Diagnosis and Treatment in Saudi Arabia

Background: Driven by Vision 2030, Saudi Arabia is rapidly integrating Artificial Intelligence into its healthcare ecosystem. This study investigates the patterns, predictors, and sociodemographic determinants of AI reliance and dependence in healthcare decision making, focusing on how trust influences the shift toward personalized digital diagnosis. Methods: A cross-sectional study was conducted with 627 adults in Saudi Arabia using convenience sampling. Data collected via online questionnaires were analyzed using JMP student edition version 18 software to evaluate user interaction with symptom checkers, wearables, and generative AI. A multidimensional framework assessed how trust and dependence influence health-seeking behaviors. Results: The findings reveal high AI engagement, with 63.7% of respondents using AI tools weekly. Conversational AI and LLMs are the dominant interfaces (92.2%), primarily serving as “gatekeepers” for personalized diagnosis (71.6%) and treatment suggestions (76.9%) before formal consultations. While gender significantly impacts reliance (p = 0.0037), trust was identified as the only significant predictor of overall engagement (p < 0.0001). Notably, age, education, and income had no statistical impact (p > 0.05), indicating uniform adoption across groups. Conclusions: For surveyed cohorts, trust is the primary determinant of AI reliance, overriding traditional demographic factors. Fostering user trust is essential for the successful implementation of AI-driven personalized healthcare solutions.