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Related Experiment Video

Updated: Jun 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Preparing for Vascular Surgery Board Certification: A Comparative Study Using Large Language Models.

Sonal Kumar1, George Y Tadros2, Taylor E Collignon3

  • 1Vascular Surgery, Ross University School of Medicine, Miramar, USA.

Cureus
|June 10, 2025
PubMed
Summary
This summary is machine-generated.

Claude 3.5 demonstrated superior performance in answering vascular surgery board exam questions compared to other large language models (LLMs). While promising, current LLMs require further development to fully support vascular surgery education.

Keywords:
ai performance evaluationamerican board of surgeryartificial intelligenceartificial intelligence in surgeryboard examscomparative studylarge language modelsvascular education and self-assessment programvascular surgeryvascular surgery education

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Surgery
  • Vascular Surgery Training

Background:

  • Large language models (LLMs) are emerging as transformative tools in medical education.
  • Limited research exists on LLM application specifically within vascular surgery training.
  • Evaluating LLMs' efficacy for vascular surgery board certification preparation is crucial.

Purpose of the Study:

  • To assess and compare the effectiveness of various LLMs in preparing for vascular surgery board certification exams.
  • To explore the potential of LLMs as supplementary educational resources in vascular surgery.

Main Methods:

  • 269 text-based multiple-choice questions from the Vascular Education and Self-Assessment Program (VESAP) version 6 were used.
  • Four AI tools (ChatGPT 3.5, Google Gemini, Microsoft Bing, Claude 3.5) were evaluated by an independent reviewer.
  • Questions were input without context into incognito AI windows; chi-square tests analyzed performance variations.

Main Results:

  • Claude 3.5 achieved the highest accuracy (65.7%), surpassing Google Gemini (55.3%), ChatGPT (55.0%), and Microsoft Bing (53.9%).
  • Claude 3.5 showed significant performance differences across disciplines (p=0.001), excelling in lower extremity (86%) and dialysis access (80%).
  • ChatGPT, Google Gemini, and Microsoft Bing did not exhibit significant accuracy variations by discipline.

Conclusions:

  • Claude 3.5 demonstrated superior performance in answering vascular surgery board exam questions (VSQE6).
  • LLMs show potential as supplementary tools, particularly for topics like lower extremity vascular issues and dialysis access.
  • Current LLM capabilities do not fully meet the evolving needs of vascular surgery education; further development is anticipated.