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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Pilot Study on Using Large Language Models for Educational Resource Development in Japanese Radiological Technologist

Tatsuya Kondo1, Masashi Okamoto1, Yohan Kondo1

  • 1Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, 951-8518 Japan.

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Summary

Large language models (LLMs) show promise for creating medical exam study materials in Japan. While effective for text-based questions, LLMs need improvements for calculations and image interpretation in medical education.

Keywords:
Artificial Intelligence in EducationEducation TechnologyLarge Language ModelsLearning EnhancementRadiological Technologist Training

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Radiology Education

Background:

  • Developing effective educational resources for medical licensure exams in non-English-speaking regions presents unique challenges.
  • Large Language Models (LLMs) offer potential solutions for content generation and knowledge dissemination.

Purpose of the Study:

  • To explore the application of LLMs in creating educational materials for the Japanese Radiological Technologist National Exam.
  • To assess the performance of LLMs in generating explanations for different question types (image-based, calculation, textual).

Main Methods:

  • Multiple-choice questions from the exam were categorized into image-based, calculation, and textual types.
  • Explanatory texts were generated using Copilot, an LLM.
  • The quality of generated explanations was assessed on a 0-4 point scale.

Main Results:

  • LLMs demonstrated high performance in generating explanations for textual questions, showcasing strong capability with specialized content.
  • Challenges were identified in generating accurate formulas and performing calculations for calculation questions.
  • Interpreting complex medical images for image-based questions also posed difficulties for the LLMs.

Conclusions:

  • LLMs have significant potential to enhance medical education resource development in non-English-speaking contexts.
  • Addressing challenges requires integrating LLMs with programming capabilities for calculations and using precise prompts for image analysis.
  • Educator involvement in validating LLM outputs and providing guidance is crucial for ensuring accuracy and effective utilization.