Objective: To examine the cross-level sociotechnical linkages between societal risk perception of medical artificial intelligence, institutional adoption patterns, and clinical safety outcomes. Specifically, this study aims to explore how social pressure shapes hospital technology strategies and to rigorously assess the association between AI usage intensity and diagnostic errors using an acute imaging-dependent condition as a specific tracer.
Methods: A cross-level analytical framework was constructed based on the Technology Acceptance Model and Institutional Theory. We integrated three heterogeneous data streams from the Federal District of Brazil: a stratified probability survey of residents (N = 4764), longitudinal hospital operational panels (1728 hospital-month observations), and a validating index of social media sentiment. A "Catchment Area Ecological Linkage" protocol was employed to merge micro-level psychometric data with meso-level organizational metrics. Structural Equation Modeling was employed to test the direct, mediating, and moderating effects among variables, with robustness and endogeneity checks conducted via time-lag analysis and double-validation. Moderators included public trust and hospital geographical remoteness.
Results: Structural equation modeling revealed a significant negative association between aggregated public risk perception and hospital AI application frequency (β = -0.34, p < 0.001), consistent with the theory of "algorithmic aversion" at the institutional level. Within the specific context of the tracer condition, higher AI usage intensity was positively associated with misdiagnosis rates (β = 0.28, p < 0.001), suggesting a pattern of "automation bias" in time-sensitive acute triage. These inhibitory effects are attenuated by high public trust and geographical remoteness.
Conclusion: Public risk perception functions as an institutional constraint that throttles technology deployment. While social pressure limits adoption, the uncritical reliance on AI in high-stakes acute settings may compromise diagnostic vigilance. This study highlights the necessity of using precise tracer conditions to evaluate digital health safety and suggests that governance must balance social legitimacy with rigorous clinical oversight.
Keywords: Artificial intelligence; algorithmic aversion; automation bias; diagnostic safety; sociotechnical systems; tracer condition.
© The Author(s) 2026.