IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0335775.html

Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study

Author

Listed:
  • Viet Anh Nguyen
  • Thi Quynh Trang Vuong
  • Van Hung Nguyen

Abstract

Background: Multimodal large-language models (LLMs) have recently gained the ability to interpret images. However, their accuracy on anatomy tasks remains unclear. Methods: A cross-sectional, atlas-based benchmark study was conducted in which six publicly accessible chat endpoints, including paired “deep-reasoning” and “low-latency” modes from OpenAI, Microsoft Copilot, and Google Gemini, identified 260 numbered landmarks on 26 high-resolution plates from a classical anatomic atlas. Each image was processed twice per model. Two blinded anatomy lecturers scored responses, including accuracy, run-to-run consistency, and per-label latency, which were compared with χ² and Kruskal–Wallis tests. Results: Overall accuracy differed significantly among models (χ² = 73.2, P

Suggested Citation

  • Viet Anh Nguyen & Thi Quynh Trang Vuong & Van Hung Nguyen, 2025. "Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-13, October.
    • Handle: RePEc:plo:pone00:0335775
    DOI: 10.1371/journal.pone.0335775
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0335775
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0335775&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0335775?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    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:plo:pone00:0335775. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.