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Large language models versus traditional textbooks: optimizing learning for plastic surgery case preparation.

Chandler Hinson1,2, Cybil Sierra Stingl3, Rahim Nazerali3

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Large language models (LLMs) offer thorough surgical education content but need improved conciseness and accuracy. ChatGPT generally outperformed Gemini, highlighting variability in AI tools for plastic surgery training.

Keywords:
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Area of Science:

  • Medical Education
  • Artificial Intelligence in Surgery
  • Plastic and Reconstructive Surgery

Background:

  • Large language models (LLMs) present a novel approach to surgical education, offering interactive learning experiences.
  • Concerns regarding LLM accuracy, knowledge depth, and potential bias necessitate rigorous evaluation.
  • This study assesses LLMs' effectiveness in plastic and reconstructive surgery training compared to traditional textbooks.

Purpose of the Study:

  • To evaluate the effectiveness of LLMs (ChatGPT-4 and Gemini) in aiding surgical trainees.
  • To compare LLM-generated content with traditional case-preparation textbooks.
  • To identify areas for LLM improvement in surgical education.

Main Methods:

  • Six representative plastic and reconstructive surgery cases were selected.
  • Questions covered clinical anatomy, indications, contraindications, and complications.
  • LLM and textbook responses were rated by medical professionals using a 5-point Likert scale.

Main Results:

  • LLM responses were more thorough but less concise than textbooks.
  • Textbooks were superior for contraindications and complications.
  • ChatGPT was rated more accurate, thorough, and useful than Gemini.

Conclusions:

  • LLMs show potential for generating thorough educational content but require enhancements in conciseness and accuracy.
  • ChatGPT generally outperformed Gemini, indicating LLM variability.
  • Further LLM development is crucial for reliable integration into medical education.