{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T08:24:55Z","timestamp":1793348695213,"version":"4.5.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patient-facing generative artificial intelligence (AI) tools such as ChatGPT are increasingly used for health information seeking, yet their role in patient\u2013physician 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Cross-sectional online survey, anonymous with respect to the investigators (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20091,044), administered in Polish via Google Forms with programmed routing; recruitment used a multi-account social-media strategy. A 24-item\n                      <jats:italic>de novo<\/jats:italic>\n                      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\u2013Whitney U, Kruskal\u2013Wallis, Spearman correlations, and logistic regression restricted to definite Yes\/No responses; the originally submitted coding and Benjamini\u2013Hochberg adjustment were retained as sensitivity analyses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Health-related AI use was reported by 84.3% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009880; 95% CI 82.0\u201386.4). Among respondents reporting AI use before a medical visit (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009519), 82.5% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009428; 95% CI 79.0\u201385.5) selected \u2018No\u2019 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 \u2018No\u2019 was similar across visit-avoidance strata (79.2\u201386.8%). Among those selecting \u2018No\u2019 who reported no AI-related visit avoidance (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009235), 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Most respondents reporting pre-consultation AI use selected \u2018No\u2019 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1933451","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:34:31Z","timestamp":1787808871000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative AI use before medical visits: disclosure-item responses, trust, and care-seeking behaviors in a cross-sectional social-media survey in Poland"],"prefix":"10.3389","volume":"8","author":[{"given":"Simona","family":"W\u00f3jcik","sequence":"first","affiliation":[{"name":"Department of Science, LUX MED Sp. z o.o.","place":["Warsaw, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Rulkiewicz","sequence":"additional","affiliation":[{"name":"LUX MED Sp. z o.o.","place":["Warsaw, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justyna","family":"Domienik-Kar\u0142owicz","sequence":"additional","affiliation":[{"name":"Department of Science, LUX MED Sp. z o.o.","place":["Warsaw, Poland"]},{"name":"Wy\u017csza Szko\u0142a Nauk Medycznych w Warszawie","place":["Warsaw, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e47564","DOI":"10.2196\/47564","article-title":"User intentions to use ChatGPT for self-diagnosis and health-related purposes: cross-sectional survey study","volume":"10","author":"Shahsavar","year":"2023","journal-title":"JMIR Hum Factors"},{"key":"B2","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12911-020-01191-1","article-title":"Use of AI-based tools for healthcare purposes: a survey study from consumers\u2019 perspectives","volume":"20","author":"Esmaeilzadeh","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"key":"B3","doi-asserted-by":"publisher","first-page":"210","DOI":"10.5694\/mja2.52598","article-title":"Use of ChatGPT to obtain health information in Australia, 2024: insights from a nationally representative survey","volume":"222","author":"Ayre","year":"2025","journal-title":"Med J Aust"},{"key":"B4","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1186\/s12913-025-13832-0","article-title":"Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study","volume":"26","author":"Abdelwahed","year":"2026","journal-title":"BMC Health Serv Res"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e58831","DOI":"10.2196\/58831","article-title":"Doctor ChatGPT, can you help me?\u2019 the patient\u2019s perspective: cross-sectional study","volume":"26","author":"Armbruster","year":"2024","journal-title":"J Med Internet Res"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","article-title":"Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine","volume":"388","author":"Lee","year":"2023","journal-title":"N Engl J Med"},{"key":"B7","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.5603\/cj.97515","article-title":"Beyond ChatGPT: what does GPT-4 add to healthcare? 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