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<lom:langstring xml:lang="x-none">10.60522/o:7212</lom:langstring>

  
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<lom:title>
  
<lom:langstring xml:lang="en">Chatbot Effectiveness in Health Education: A  Comparative Study of Gemini, ChatGPT, and Llama</lom:langstring>

  
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<lom:description>
  
<lom:langstring xml:lang="en">This paper presents a focused analysis of the application of AI-driven chatbots in health education, drawing 
upon a comparative evaluation of OpenAI’s ChatGPT, Google’s Gemini, and Meta’s Llama. The study 
examines existing literature on the efficacy of large language models (LLMs) in healthcare, emphasizing 
their potential and challenges. Through a detailed expert evaluation, the response quality of these chatbots 
to health-related queries is assessed based on medical correctness, safety, and user satisfaction.
Findings from the expert evaluations indicate that while ChatGPT and Gemini consistently deliver 
responses closely aligned with established medical guidelines, Llama2 shows limitations in clarity and 
precision. The literature review underscores the rapid advancements in AI technology, yet it also highlights 
the ongoing concerns about data privacy, response accuracy, and the risk of misinformation in medical 
contexts.
This paper contributes to the growing discourse on integrating AI in healthcare, proposing that while AI 
chatbots hold promise for enhancing health education and accessibility, careful consideration of their 
limitations and ethical implications is crucial for their effective deployment.</lom:langstring>

  
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<lom:langstring xml:lang="en">Large Language Models</lom:langstring>

  
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<lom:langstring xml:lang="en">AI</lom:langstring>

  
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<lom:langstring xml:lang="en">Assessment</lom:langstring>

  
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VERSION:3.0
N:Kramml;Patrick;
FN:Patrick Kramml
X-ORCID:https://orcid.org/0009-0007-2984-9461
END:VCARD</lom:vcard>

  
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<lom:langstring xml:lang="en">SOCIAL SCIENCES</lom:langstring>

  
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<lom:entry>
  
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<lom:entry>
  
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