Enhancing Anesthetic Patient Education Through the Utilization of Large Language Models for Improved Communication and Understanding

Abstract

Background/Objectives: The rapid development of Large Language Models (LLMs) presents promising applications in healthcare, including patient education. In anesthesia, where patient anxiety is common due to misunderstandings and fears, LLMs could alleviate perioperative anxiety by providing accessible and accurate information. This study explores the potential of LLMs to enhance patient education on anesthetic and perioperative care, addressing time constraints faced by anesthetists. Methods: Three language models—ChatGPT-4, Claude 3, and Gemini—were evaluated using three common patient prompts. To minimize bias, incognito mode was used. Readability was assessed with the Flesch–Kincaid, Flesch Reading Ease, and Coleman–Liau indices. Response quality was rated for clarity, comprehension, and informativeness using the DISCERN score and Likert Scale. Results: Claude 3 required the highest reading level, delivering detailed responses but lacking citations. ChatGPT-4o offered accessible and concise answers but missed key details. Gemini provided reliable and comprehensive information and emphasized professional guidance but lacked citations. According to DISCERN and Likert scores, Gemini had the highest rank for reliability and patient friendliness. Conclusions: This study found that Gemini provided the most reliable information, followed by Claude 3, although no significant differences were observed. All models showed limitations in bias and lacked sufficient citations. While ChatGPT-4o was the most comprehensible, it lacked clinical depth. Further research is needed to balance simplicity with clinical accuracy, explore Artificial Intelligence (AI)–physician collaboration, and assess AI’s impact on patient safety and medical education.

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