Public perspectives on artificial intelligence in healthcare in the USA: a cross-sectional survey

Abstract

Abstract Objective To evaluate public perspectives on artificial intelligence (AI) in healthcare in the USA and assess variation in trust, comfort and perceived fairness across demographic groups. Methods and analysis This cross-sectional survey of US adults was conducted between 21 November and 4 December 2025, via YouGov, a private public opinion research firm, using a non-probability-based online panel. The sample included 500 general population respondents and an oversample of 750 adults aged 60+. Survey weights were applied to approximate the US adult population. Responses were measured using Likert-type or categorical scales and analysed using survey-weighted statistical tests with false discovery rate (FDR) correction for multiple comparisons. Primary outcomes included comfort with healthcare providers using AI, attitudes towards AI-generated second opinions, prior use of AI for healthcare, relative trust in AI versus clinicians for health information, preference for AI chatbots versus providers in explaining care plans and perceived effects of AI on healthcare fairness. Results Among weighted respondents (n=500), 34.0% reported prior healthcare AI use, which was more common among college graduates than those without a high school diploma (46.5% vs 22.9%). Only 25.4% reported being comfortable or very comfortable with their provider using AI, while 43.0% were uncomfortable and 31.5% were neutral. Regarding information trust, 44.5% trusted AI much less than clinicians; 88.4% reported a provider would better help them understand their care plan than a chatbot. Prior AI use was associated with more favourable attitudes towards laboratory/imaging interpretation and AI-assisted referrals. Differences by political affiliation, insurance, health status and age were not statistically significant after FDR correction. Conclusions AI implementation appears to be outpacing public trust. Higher educational attainment was the most consistent predictor of favourable attitudes. Notably, one-third of respondents were neutral across outcomes, suggesting public attitudes are potentially modifiable. These findings underscore the need for transparent deployment and targeted trust-building strategies to support equitable AI adoption in healthcare.