Clinical Report: Assessing the Reliability and Readability of Pediatric Anesthesia Guidance
Overview
This study evaluates the reliability and readability of pediatric anesthesia information provided by four large language models (LLMs). Findings indicate variability in content quality and readability.
Background
Pediatric anesthesia presents unique challenges due to the physiological differences between children and adults, as well as high levels of perioperative anxiety among pediatric patients. Caregivers often seek online information, but the quality of this information can be inconsistent. The rise of LLMs as sources of health information necessitates an evaluation of their reliability and readability.
Data Highlights
No numerical data was provided in the source material.
Key Findings
The study systematically evaluated four widely used LLMs: ChatGPT, Claude, DeepSeek, and Gemini.
LLM-generated health information often demonstrates limited readability and occasional factual inconsistencies.
Excessive linguistic complexity in LLM responses may hinder caregivers' understanding of anesthesia-related risks.
There is a critical need for accurate and clear information for caregivers regarding pediatric anesthesia.
Clinical Implications
Healthcare professionals should be aware of the potential inaccuracies in LLM-generated information regarding pediatric anesthesia.
Conclusion
The evaluation of LLMs in pediatric anesthesia highlights concerns regarding the reliability and readability of the information they provide.
Google Trends data showed increased US search interest in vitamin A and cod liver oil during the 2025 measles outbreak, but normalized search data cannot determine actual product use, dosing behavior, or harms.
