{"@context":{"aiiso":"http:\/\/purl.org\/vocab\/aiiso\/schema#","arm":"https:\/\/ld4p.github.io\/arm\/core\/ontology\/0.1\/","bf":"http:\/\/id.loc.gov\/ontologies\/bibframe\/","bib":"https:\/\/bibliotek-o.org\/","bibo":"http:\/\/purl.org\/ontology\/bibo\/","cito":"http:\/\/purl.org\/spar\/cito\/","classSchemes":"http:\/\/id.loc.gov\/vocabulary\/classSchemes","dbo":"http:\/\/dbpedia.org\/ontology\/","dce":"http:\/\/purl.org\/dc\/elements\/1.1\/","dcmitype":"http:\/\/dublincore.org\/documents\/2000\/07\/11\/dcmi-type-vocabulary\/#","dcterms":"http:\/\/purl.org\/dc\/terms\/","ebucore":"http:\/\/www.ebu.ch\/metadata\/ontologies\/ebucore\/ebucore","edm":"http:\/\/www.europeana.eu\/schemas\/edm\/","foaf":"http:\/\/xmlns.com\/foaf\/spec\/#","frap":"http:\/\/purl.org\/cerif\/frapo","identifiers":"http:\/\/id.loc.gov\/vocabulary\/identifiers","ids":"http:\/\/id.loc.gov\/vocabulary\/identifiers\/","opaque":"http:\/\/opaquenamespace.org\/","pcdm":"http:\/\/pcdm.org\/models#","phaidra":"https:\/\/phaidra.org\/ontology\/","rdam":"http:\/\/rdaregistry.info\/Elements\/m\/","rdau":"http:\/\/rdaregistry.info\/Elements\/u\/","rdax":"http:\/\/rdaregistry.info\/Elements\/x\/","rdf":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#","rdfs":"https:\/\/www.w3.org\/TR\/rdf-schema\/","relators":"http:\/\/id.loc.gov\/vocabulary\/relators","role":{"@context":{"aut":{"@container":"@list","@id":"http:\/\/id.loc.gov\/vocabulary\/relators\/aut"}}},"schema":"http:\/\/schema.org\/","skos":"http:\/\/www.w3.org\/2004\/02\/skos\/core#","skosxl":"http:\/\/www.w3.org\/2008\/05\/skos-xl"},"@id":"https:\/\/phaidra.ustp.at\/o:7212","bf:note":[{"@type":"bf:Note","skos:prefLabel":[{"@language":"eng","@value":"This paper presents a focused analysis of the application of AI-driven chatbots in health education, drawing \nupon a comparative evaluation of OpenAI’s ChatGPT, Google’s Gemini, and Meta’s Llama. The study \nexamines existing literature on the efficacy of large language models (LLMs) in healthcare, emphasizing \ntheir potential and challenges. Through a detailed expert evaluation, the response quality of these chatbots \nto health-related queries is assessed based on medical correctness, safety, and user satisfaction.\nFindings from the expert evaluations indicate that while ChatGPT and Gemini consistently deliver \nresponses closely aligned with established medical guidelines, Llama2 shows limitations in clarity and \nprecision. The literature review underscores the rapid advancements in AI technology, yet it also highlights \nthe ongoing concerns about data privacy, response accuracy, and the risk of misinformation in medical \ncontexts.\nThis paper contributes to the growing discourse on integrating AI in healthcare, proposing that while AI \nchatbots hold promise for enhancing health education and accessibility, careful consideration of their \nlimitations and ethical implications is crucial for their effective deployment."}]}],"bf:provisionActivity":[{"@type":"bf:Publication","bf:agent":[{"@type":"schema:Organization","schema:name":[{"@value":"St. Pölten University of Applied Sciences"}]}],"bf:date":["2024-11-27"],"bf:place":[{"@type":"schema:Place","skos:prefLabel":[{"@value":"St. Pölten"}]}]}],"dce:subject":[{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Large Language 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