Using first-wave data from an ongoing longitudinal panel of 3,915 respondents, this study applies structural equation modeling to examine public perceptions of automated decision-making in healthcare. Perceived helpfulness, risk, and fairness were associated with AI familiarity, AI literacy, use of conversational agents for health information, traditional digital health information use, and confidence that clinicians can distinguish AI-generated from human-generated content. The findings suggest that perceived helpfulness and fairness depend more strongly on trust in healthcare professionals and human oversight than on trust in the technology itself.
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