{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T15:11:43Z","timestamp":1789053103808,"version":"build-2803163510"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"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:p>The widespread availability of generative artificial intelligence (AI) has substantially reshaped the health-information environment compared with the COVID-19 pandemic. Contemporary AI tools can rapidly generate highly realistic medical-looking content from text, including laboratory reports, radiological images, histological slides, and institutional clinical documentation. During infectious disease outbreaks, such simulated medical content may exploit the visual authority traditionally associated with healthcare institutions and influence public risk perception. During media coverage of the May 2026 Andes virus (ANDV) alert in Italy, news outlets published realistic AI-generated medical imagery depicting hantavirus diagnostic reports, hospital-associated laboratory materials, and outbreak-response scenes linked to real healthcare institutions. One image, in particular, reproduced the visual identity of a real laboratory report produced at \u201cLuigi Sacco\u201d University Hospital despite displaying multiple inconsistencies. In some additional examples, AI generation was not clearly disclosed despite the use of recognizable institutional names and logos. This phenomenon has the potential to extend beyond isolated misinformation events. Emerging evidence indicates that experts may struggle to reliably distinguish AI-generated biomedical and radiological images from authentic materials, raising concern that future infectious disease outbreaks may occur within an information ecosystem increasingly shaped by visually persuasive synthetic media. Such content may amplify public anxiety, distort perceptions of authority, and erode trust in institutions. Synthetic medical realism should therefore be considered a potential emerging category of public-health risk during infectious disease emergencies that requires coordinated action from institutions and regulators developing verification systems, while news media, platforms, and publishers implement AI-disclosure standards and guidelines for synthetic biomedical content.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1887820","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:51:12Z","timestamp":1789051872000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-generated medical realism and hantavirus misinformation: a new public health threat?"],"prefix":"10.3389","volume":"8","author":[{"given":"Marta","family":"Colaneri","sequence":"first","affiliation":[{"name":"Department of Clinical and Biomedical Sciences, University of Milan","place":["Milan, Italy"]},{"name":"Department of 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