J Med Internet Res. 2026 Sep 16;28:e98326. doi: 10.2196/98326.
ABSTRACT
BACKGROUND: Generative AI (GenAI) is increasingly used by health information consumers to interpret medical content and support decision-making. Although these systems provide accessible and timely information, they may also produce inaccurate or misleading outputs. Effective use of GenAI, therefore, depends on users’ ability to calibrate trust based on information accuracy. However, little is known about how learned dependency on GenAI (the habitual reliance on AI systems for solving problems) influences trust calibration in health information contexts.
OBJECTIVE: This study examines how learned dependency on GenAI affects health information consumers’ calibration of trust in AI-generated information, and whether text-based visual attention cues (TVCs), such as highlighting critical information in text, mitigate overreliance on incorrect outputs.
METHODS: We conducted 2 randomized controlled experiments: the first involved 338 college students, and the second replicated the study with 563 Amazon Mechanical Turk participants. Both studies used a 2 × 2 between-participants design, manipulating (1) information accuracy (correct vs incorrect) and (2) TVCs (text highlight vs no text highlight). Participants evaluated AI-generated health information presented alongside source text. Trust was measured using a multi-item scale, and learned dependency on GenAI was assessed using a validated self-reported measure. Linear regression models were used to examine main and interaction effects.
RESULTS: Across both experiments, information accuracy had a significant positive effect on trust, with participants expressing greater trust in correct than in incorrect AI-generated information (experiment 1: B=2.107, 95% CI 1.337-2.878; P<.001 and experiment 2: B=0.203, 95% CI 0.115-0.290; P<.001). Learned dependency was positively associated with trust in both experiments (experiment 1: B=0.277, 95% CI 0.033-0.521; P=.03 and experiment 2: B=0.822, 95% CI 0.715-0.929; P<.001), such that users with greater dependency trusted AI outputs more overall. Critically, the interaction between information accuracy and learned dependency was negative and significant in both experiments (experiment 1: B=-0.399, 95% CI -0.695 to -0.104; P<.001 and experiment 2: B=-0.459, 95% CI -0.577 to -0.340; P<.001), indicating that higher dependency reduces users’ sensitivity to information inaccuracy. Text highlighting did not significantly affect trust in either experiment, nor did it moderate the relationship between learned dependency and trust.
CONCLUSIONS: This study demonstrates that while users generally trust accurate AI-generated health information more than inaccurate information, higher self-reported learned dependency is associated with weaker trust calibration, predicting greater susceptibility to incorrect outputs. TVCs, such as text highlighting, are insufficient to mitigate this effect. These findings highlight the need for more effective design interventions to support critical evaluation and reduce overreliance on GenAI in health information environments.
PMID:42747973 | DOI:10.2196/98326
