Generative AI use before medical visits: disclosure-item responses, trust, and care-seeking behaviors in a cross-sectional social-media survey in Poland

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Background Patient-facing generative artificial intelligence (AI) tools such as ChatGPT are increasingly used for health information seeking, yet their role in patient–physician communication remains insufficiently characterized. Using the Network Episode Model as the primary framework, this study examined self-reported pre-consultation AI use, whether respondents reported disclosing that use to a physician, and self-reported AI-associated health-behavior change in a convenience sample of Polish adults recruited via social media. Findings are exploratory. Methods Cross-sectional online survey, anonymous with respect to the investigators ( N = 1,044), administered in Polish via Google Forms with programmed routing; recruitment used a multi-account social-media strategy. A 24-item de novo questionnaire assessed health-related and pre-consultation AI use, disclosure-item responses, trust, self-reported behavioral consequences, and sociodemographics. Q13 responses were analyzed among respondents reporting AI use before a medical visit; the survey did not confirm that a consultation subsequently occurred. Exploratory analyses included chi-square, Mann–Whitney U, Kruskal–Wallis, Spearman correlations, and logistic regression restricted to definite Yes/No responses; the originally submitted coding and Benjamini–Hochberg adjustment were retained as sensitivity analyses. Results Health-related AI use was reported by 84.3% ( n = 880; 95% CI 82.0–86.4). Among respondents reporting AI use before a medical visit ( n = 519), 82.5% ( n = 428; 95% CI 79.0–85.5) selected ‘No’ when asked whether they had informed a physician about that use. Because attendance was not confirmed, this proportion cannot be interpreted as a rate of what patients withheld during completed clinical encounters. Selection of ‘No’ was similar across visit-avoidance strata (79.2–86.8%). Among those selecting ‘No’ who reported no AI-related visit avoidance ( n = 235), the most frequent reasons were fear of a negative physician reaction (86.4%) and reluctance to undermine physician authority (74.9%). In primary definite-response models, male gender and greater AI trust were associated with the outcomes; AI trust was strongly negatively associated with age. Apparent discrimination and explained variation were modest. Conclusions Most respondents reporting pre-consultation AI use selected ‘No’ on the disclosure item. Because consultation attendance and a defined index episode were not confirmed, the actual disclosure rate within completed clinical encounters cannot be estimated from this survey. The findings advance a hypothesis about a possible communication barrier requiring prospective, episode-based study.

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