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dc.contributor.authorGutowski A.-
dc.contributor.authorCarrion D.-
dc.contributor.authorBadawy M.-
dc.date.accessioned2026-04-27T03:26:48Z-
dc.date.available2026-04-27T03:26:48Z-
dc.date.copyright2026-
dc.date.issued2026-04-27en
dc.identifier.urihttps://repository.monashhealth.org/monashhealthjspui/handle/1/58149-
dc.description.abstractLarge language models (LLMs) are becoming increasingly utilised in healthcare, especially to generate patient information. They offer a potential opportunity to generate accessible, patient-centred information on radiation risks. However, no study had previously evaluated whether LLMs could effectively communicate these risks to consumers in radiology contexts — representing a clear gap in supporting informed consent and meaningful consumer participation in care decisions.-
dc.subject.meshhealth communication-
dc.titleTalking risk: how well do LLMs commmunciation radiation exposure-
dc.typePoster-
dc.identifier.affiliationRadiology-
dc.description.conferencenameMonash Health Consumer Participation Poster Competition-
item.openairetypePoster-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptRadiology-
Appears in Collections:Consumer Participation Poster Competition
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