Can ChatGPT provide quality information about fever in children?

J Paediatr Child Health

Department of Biostatics, Erciyes University, Faculty of Medicine, Kayseri, Turkey.

Published: January 2025

AI Article Synopsis

  • The study explores how AI systems, specifically ChatGPT, can enhance medical information related to fever in children.* -
  • Researchers assessed the quality and readability of ChatGPT's responses to the 50 most commonly asked questions about fever, finding them to be high-quality and reliable.* -
  • A positive correlation was observed between different readability scores, indicating that ChatGPT's answers are not only informative but also easy for patients and caregivers to understand.*

Article Abstract

Background: Artificial intelligence (AI) systems hold great promise in improving medical care and health problems.

Aim: We aimed to evaluate the answers by asking the most frequently asked questions to ChatGPT for the prediction and treatment of fever, which is a major problem in children.

Methods: The 50 questions most frequently asked about fever in children were determined, and we asked them to ChatGPT. We evaluated the responses using the quality and readability scales.

Results: While ChatGPT demonstrated good quality in its responses, the readability scale and the Patient Education Material Evaluation Tool (PEMAT) scale used with materials appearing on the site were also found to be successful. Among the scales in which we evaluated ChatGPT responses, a weak positive relationship was found between Gunning Fog (GFOG) and Simple Measure of Gobbledygook (SMOG) scores (r = 0.379) and a significant and positive relationship was found between FGL and SMOG scores (r = 0.899).

Conclusion: This study sheds light on the quality and readability of information regarding the presentation of AI tools, such as ChatGPT, regarding fever, a common complaint in children. We determined that the answers to the most frequently asked questions about fire were high-quality, reliable, easy to read and understandable.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701195PMC
http://dx.doi.org/10.1111/jpc.16710DOI Listing

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