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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.0//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/static/PubMed.dtd">
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>Barw</PublisherName>
      <JournalTitle>Barw Medical Journal</JournalTitle>
      <Issn>2960-1959</Issn>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>05</Month>
        <Day>07</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Assessment of Nursing Skill and Knowledge of ChatGPT, Gemini, Microsoft Copilot, and Llama: A Comparative Study</ArticleTitle>
    <ELocationID EIdType="doi">10.58742/bmj.v2i2.87</ELocationID>
    <Language>eng</Language>
    <AuthorList>
      <Author>
        <FirstName EmptyYN="Y"/>
        <LastName>Dilan S. Hiwa</LastName>
        <Affiliation>College of Medicine, University of Sulaimani, Madam Mitterrand Street, Sulaymaniyah, Kurdistan, Iraq. dilan.sarmad.hiwa@gmail.com</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="Y"/>
        <LastName>Sarhang Sedeeq Abdalla</LastName>
        <Affiliation>Smart Health Tower, Madam Mitterrand Street, Sulaymaniyah, Kurdistan, Iraq. sarhang.17000584@univsul.edu.iq</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="Y"/>
        <LastName>Aso S. Muhialdeen</LastName>
        <Affiliation>Smart Health Tower, Madam Mitterrand Street, Sulaymaniyah, Kurdistan, Iraq. aso.muhialdeen@gmail.com</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="Y"/>
        <LastName>Hussein M. Hamasalih</LastName>
        <Affiliation>College of Nursing, University of Sulaimani, Sulaymaniyah, Kurdistan, Iraq. hussein.hama@gmail.com</Affiliation>
      </Author>
      <Author>
        <FirstName EmptyYN="Y"/>
        <LastName>Sanaa O. Karim</LastName>
        <Affiliation>College of Nursing, University of Sulaimani, Madam Mitterrand Street, Sulaymaniyah, Kurdistan, Iraq. sanaa.karim@gmail.com</Affiliation>
      </Author>
    </AuthorList>
    <History>
      <PubDate PubStatus="received">
        <Year>2024</Year>
        <Month>04</Month>
        <Day>02</Day>
      </PubDate>
    </History>
    <Abstract>Introduction

Artificial intelligence (AI) has emerged as a transformative force in healthcare. This study assesses the performance of advanced AI systems&#x2014;ChatGPT-3.5, Gemini, Microsoft Copilot, and Llama 2&#x2014;in a comprehensive 100-question nursing competency examination. The objective is to gauge their potential contributions to nursing healthcare education and future potential implications.

Methods

The study tested four AI systems (ChatGPT 3.5, Gemini, Microsoft Copilot, Llama 2) with a 100-question nursing exam in February of 2024. A standardized protocol was employed to administer the examination, covering diverse nursing competencies. Questions derived from reputable clinical manuals ensured content reliability. The AI systems underwent evaluation based on accuracy rates.

Results

Microsoft Copilot demonstrated the highest accuracy at 84%, followed by ChatGPT 3.5 (77%), Gemini (75%), and Llama 2 (68%). None achieved complete accuracy on all questions. Each of the AI systems has answered at least one question that only they got correctly.

Conclusion

The variations in AI answers underscore the significance of selecting appropriate AI systems based on specific application requirements and domains, as no singular AI system consistently surpassed others in every aspect of nursing knowledge.
</Abstract>
  </Article>
</ArticleSet>
