Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jul 30, 2026
Open Peer Review Period: Jul 30, 2026 - Sep 24, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Trust Recalibration in AI-Enabled Health Decision Support: A National Survey of Risk-Dependent AI Reliance and Human Oversight Preferences
ABSTRACT
Background:
Artificial intelligence (AI)-enabled health decision support systems are increasingly integrated into digital health services, supporting symptom assessment, health information seeking, and medical decision assistance. However, existing technology acceptance models primarily describe adoption as a gradual process driven by perceived usefulness, usability, and performance expectations. These models provide limited explanations for why individuals may readily use AI for routine health information tasks while preferring human oversight when AI systems become involved in higher-risk medical decisions.
Objective:
This study aimed to investigate nonlinear trust recalibration patterns in AI-enabled health decision support and examine how perceived decision risk and digital health literacy influence AI reliance and preferences for human oversight.
Methods:
A nationwide cross-sectional survey was conducted among 2,009 Chinese adults using an online questionnaire platform. Participants evaluated AI-enabled health decision scenarios involving different levels of perceived clinical risk. Associations between decision risk, digital health literacy, trust-related factors, and AI reliance patterns were examined using multivariate regression, correlation analyses, and nonlinear transition analyses.
Results:
The findings demonstrated a nonlinear trust recalibration pattern in AI-enabled health decision support. Participants showed relatively high reliance on AI systems for low-risk information-processing tasks; however, AI reliance decreased substantially when decisions involved greater uncertainty, responsibility, and potential health consequences, representing a “Trust Cliff” phenomenon. Furthermore, digitally literate users demonstrated a dual cognitive pattern: greater AI utilization was accompanied by increased scrutiny regarding algorithmic limitations, transparency, privacy protection, and governance, representing a “Rational Paradox”.
Conclusions:
AI acceptance in digital health does not follow a simple linear progression. Instead, users continuously recalibrate trust according to decision risk, perceived responsibility, and cognitive evaluation capacity. Sustainable implementation of AI-enabled health systems requires balancing technological capability with appropriate human oversight, transparency, and accountability mechanisms.
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Copyright
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