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Related Experiment Video

Updated: Sep 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

693

Large Language Model-Assisted Surgical Consent Forms in Non-English Language: Content Analysis and Readability

Namkee Oh1, Jongman Kim1, Sunghae Park1

  • 1Department of Surgery, Samsung Medical Center, 81 Ilwonro, Seoul, Republic of Korea, 82 1093650277.

Journal of Medical Internet Research
|June 19, 2025
PubMed
Summary

Large language models (LLMs) improve surgical consent form readability but may reduce critical risk information. A balanced approach is needed to ensure accuracy and accessibility in AI-assisted medical documents.

Keywords:
ChatGPT-4oinformed consentlarge language modelliver resectionnatural language processingoperativereadabilitysurgical consent formsurgical procedures

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Patient Communication

Background:

  • Complex language in surgical consent forms hinders patient comprehension.
  • Large language models (LLMs) offer potential for simplifying medical information.
  • Limited evidence exists on LLM impact on non-English consent form content preservation.

Purpose of the Study:

  • To evaluate the effect of LLM-assisted editing on the readability and content quality of Korean surgical consent forms.
  • Specifically assess changes in standardized liver resection consent documents across multiple institutions.

Main Methods:

  • Collected standardized liver resection consent forms from 7 South Korean institutions.
  • Simplified forms using ChatGPT-4o and assessed readability (KReaD, Natmal indices).
  • Evaluated content quality (risk, benefit, alternatives) via specialist review and statistical analysis.

Main Results:

  • AI-assisted editing significantly improved readability scores (KReaD, Natmal) and reduced sentence length and difficult word ratios.
  • Content quality analysis revealed a decline in risk description and overall impression scores.
  • Statistical models confirmed significant reductions in risk disclosure and overall impression, indicating potential omissions.

Conclusions:

  • LLM-assisted surgical consent forms enhance readability but may compromise content completeness, especially risk disclosure.
  • A balanced approach is crucial to maintain accessibility while ensuring medical and legal accuracy.
  • Further research should focus on patient comprehension, informed decision-making, and multilingual validation.