IDEAS home Printed from https://ideas.repec.org/a/ids/ijcast/v1y2025i4p382-396.html
   My bibliography  Save this article

Evaluating the effectiveness of large language models in medicine education: a comparison of current medicine knowledge

Author

Listed:
  • Md. Mahadi Hassan
  • Noushin Nohor

Abstract

Recent advancements in artificial intelligence have led to the development of powerful large language models (LLMs) like ChatGPT-4-turbo, Gemini 2.0 Flash, DeepSeek-R1, and Qwen2.5-Max. This study evaluates their medical knowledge proficiency using multiple-choice questions (MCQs) sourced from a reputable medical textbook, with answers verified by experts. Each model was tested on its ability to select correct answers, and performance was analysed using ANOVA and Tukey's HSD tests. Results showed that while all models exhibited some proficiency, ChatGPT-4-turbo significantly outperformed Gemini 2.0 Flash and Qwen2.5-Max, with no notable difference between ChatGPT-4-turbo and DeepSeek-R1. Despite their capabilities, these models remain unreliable for medical education and assistance. Enhancing their accuracy and reliability is crucial for their effective application in healthcare, enabling medical students and professionals to utilise AI for learning and clinical decision-making. Further development is needed to improve their utility in medical practice.

Suggested Citation

  • Md. Mahadi Hassan & Noushin Nohor, 2025. "Evaluating the effectiveness of large language models in medicine education: a comparison of current medicine knowledge," International Journal of Complexity in Applied Science and Technology, Inderscience Enterprises Ltd, vol. 1(4), pages 382-396.
    • Handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:382-396
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=147091
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijcast:v:1:y:2025:i:4:p:382-396. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=71 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.