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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Exploring body donation communication with large language models: Accuracy, readability, and ethical considerations.

Fulya Temizsoy Korkmaz1, Fatma Ok1, Burak Karip1

  • 1Department of Anatomy, Hamidiye Faculty of Medicine, University of Health Sciences, Istanbul, Turkey.

Anatomical Sciences Education
|September 7, 2025
PubMed
Summary

Large language models (LLMs) can generate accurate and readable educational materials for whole-body donation. Top models like ChatGPT-4o and Grok3.0 show promise in supplementing donor communications, improving accessibility.

Keywords:
anatomy educationartificial intelligencebody donationethicslarge language modelsmodified DISCERNreadability

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Area of Science:

  • Anatomical Sciences
  • Medical Education
  • Artificial Intelligence

Background:

  • Educational materials for whole-body donation are crucial for anatomy programs but face supply shortages.
  • Existing communication methods may not meet the demand for accurate, accessible information on body donation.
  • Large Language Models (LLMs) offer a potential solution to enhance donor communication and educational material creation.

Purpose of the Study:

  • To investigate the efficacy of LLM-based approaches in generating effective communication materials for whole-body donation.
  • To provide a comparative quantitative benchmark and evaluation framework for LLM performance in this domain.
  • To assess the accuracy, quality, readability, and vocabulary diversity of LLM-generated responses to body donation FAQs.

Main Methods:

  • Five LLMs (ChatGPT-4o, Grok3.0, Claude4Sonnet, Gemini2.5 Flash, DeepSeekR1) generated Turkish responses to six common body donation questions.
  • Four anatomists evaluated the generated content based on accuracy, quality, readability, and vocabulary diversity.
  • Statistical analysis was performed to compare the performance differences between the LLM models.

Main Results:

  • ChatGPT-4o and Grok3.0 significantly outperformed other models in quality scores (mean checklist scores 21.7 ± 2.8 and 21.0 ± 5.1, respectively).
  • Top-performing LLMs produced content at a below-secondary-school reading level, indicating high readability.
  • LLM-generated materials demonstrated potential for promoting whole-body donation by providing accessible and reliable information.

Conclusions:

  • LLM-produced materials can effectively supplement whole-body donation information, potentially streamlining content creation and reducing staff workload.
  • Ethical transparency, cultural sensitivity, and continuous human oversight are crucial safeguards for LLM use in sensitive donation contexts.
  • Clear governance frameworks, expert audits, and disclosed quality metrics are recommended for responsible LLM implementation in body donation communication.