Abstract
BACKGROUND: Large language models (LLMs), such as ChatGPT-4o, Grok 3.0, Gemini Advanced 2.0 Pro, and DeepSeek, have been tested in many medical domains in recent years, ranging from clinical decision support systems to patient information processes and even some intraoperative scenarios. However, despite this widespread use, how LLMs perform as step-by-step guides in environments requiring sensory-motor interaction-such as direct cadaver dissection-has not yet been systematically evaluated. This gap is particularly pronounced in anatomically complex areas with low error tolerance, such as brachial plexus dissection. This study aimed to comparatively analyze the performance of four different large language models (LLMs) in terms of scientific quality, educational value, and readability of their responses to structured questions in a cadaver dissection environment. METHODS: A structured question set of 28 items on brachial plexus dissection was created. Four experienced anatomists blindly evaluated the responses from the models using the modified DISCERN scale (mDISCERN) and the Global Quality Score (GQS). Readability was assessed using Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), Gunning Fog Index (GFI), and Coleman-Liau Index (CLI). Content validity was tested via the Content Validity Index (CVI), and inter-rater reliability was calculated using Intraclass Correlation Coefficient (ICC) and Cohen's Kappa. RESULTS: ChatGPT-4o and Grok 3.0 received the highest scores for scientific accuracy and guidance structure (p < 0.01). DeepSeek showed high readability but limited content depth, while Gemini performed moderately across all parameters. Readability metrics were significantly correlated with quality scores. CONCLUSION: This is one of the first studies to systematically examine how LLM-based systems perform in a training context with sensory challenges such as cadaver dissection. While LLMs cannot replace the ethical and educational value provided by real human donors, they may offer scalable, individualized support in settings with limited mentorship or cadaver availability. Our study not only aims to support anatomy education in resource-limited environments, but also serves as a foundational reference for future AI-assisted cadaveric studies and intraoperative decision-support models in surgical anatomy.