{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T01:30:30Z","timestamp":1793669430349,"version":"4.5.1"},"reference-count":82,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Public perception plays an important role in the responsible implementation of artificial intelligence (AI) in healthcare because trust, perceived risk, and expectations regarding human\u2013AI collaboration may influence the acceptance of AI-assisted medical services. This study aimed to examine public discourse and sentiment regarding AI in healthcare using large-scale Reddit discussions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective content analysis of 36,555 Reddit posts and comments published between March 1, 2020, and March 31, 2025. Reddit was used as a source of large-scale, spontaneous, user-generated discussions. BERTopic modeling was applied to identify latent discussion topics. Topics were interpreted based on semantic similarity, representative keywords, and representative paraphrased posts, and were subsequently grouped into thematic domains. Sentiment analysis and temporal trend analysis were also performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fourteen discussion topics were identified across six thematic domains: human-centered healthcare, auxiliary medical services, AI platforms and tools, cultural perceptions, food and health safety, and medical regulation. Overall sentiment distribution was 41.4% positive, 23.8% neutral, and 35.1% negative, indicating a generally positive orientation while also revealing substantial public concern. Negative sentiments were primarily associated with technological maturity, commercialization, privacy and safety risks, and the potential displacement of physicians. Temporal analysis demonstrated changes in sentiment distribution over time, particularly following the widespread public diffusion of generative AI tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings suggest that public attitudes toward medical AI are simultaneously optimistic and cautious. Concerns regarding governance, safety, commercialization, and workforce implications remain prominent in online discussions. These results highlight the importance of transparent communication, clearer regulatory governance, and careful workforce planning to support the responsible integration of AI into healthcare systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1839898","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:43:39Z","timestamp":1780379019000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Public perceptions of AI in healthcare: a large-scale BERTopic and sentiment analysis of Reddit discussions"],"prefix":"10.3389","volume":"14","author":[{"given":"Zaiyu","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Philosophy, Xi\u2019an Jiaotong University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Philosophy, Xi\u2019an Jiaotong University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Marxism, Party School of the CPC Shanxi Provincial Committee (Shanxi Academy of Governance)","place":["Taiyuan, Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahe","family":"Liang","sequence":"additional","affiliation":[{"name":"3D Printing Research Center, Department of Ultrasound Medicine, The Second Affiliated Hospital of Air Force Medical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yandong","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The Second Affiliated Hospital of Air Force Medical University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.inffus.2023.03.008","article-title":"A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion","volume":"96","author":"Albahri","year":"2023","journal-title":"Inf Fusion"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","article-title":"Revolutionizing healthcare: the role of artificial intelligence in clinical practice","volume":"23","author":"Alowais","year":"2023","journal-title":"BMC Med Educ"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","article-title":"Artificial intelligence in healthcare: review and prediction case 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