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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Artificial intelligence performance in pediatric asthma.

Ece Şenbaykal Yiğit1, İlke Taşkırdı1,2, İdil Akay Hacı1

  • 1Department of Pediatrics, Division of Pediatric Allergy and Immunology, Izmir City Hospital, Izmir, Turkey.

The Journal of Asthma : Official Journal of the Association for the Care of Asthma
|July 11, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) tools like ChatGPT-4o offer reliable and high-quality information for pediatric asthma. However, the complex language used may present readability challenges for parents seeking asthma guidance.

Keywords:
ChatGPTchildhoodqualityreadabilityreliability

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Area of Science:

  • Pediatric Pulmonology
  • Artificial Intelligence in Healthcare
  • Medical Information Accessibility

Background:

  • Childhood asthma is a prevalent chronic respiratory condition with significant symptom burden.
  • Artificial intelligence (AI) applications, including ChatGPT, are increasingly integrated into various sectors, including healthcare information dissemination.
  • Evaluating the utility of AI-generated content for specific medical conditions is crucial for its effective application.

Purpose of the Study:

  • To assess the reliability and quality of ChatGPT-4o's responses to pediatric asthma queries.
  • To evaluate the readability of AI-generated information concerning childhood asthma.
  • To determine the suitability of ChatGPT-4o as a resource for parents and caregivers of children with asthma.

Main Methods:

  • ChatGPT-4o was queried with 25 common questions about pediatric asthma.
  • Answer reliability and quality were evaluated using the Global Quality Scale and modified DISCERN tool.
  • Readability was assessed using seven established indices, including the Flesch-Kincaid Grade Level (FKGL).

Main Results:

  • ChatGPT-4o demonstrated high reliability (84-88%) and quality (88%) in its responses.
  • The application's answers required a high level of reading proficiency, indicated by an FKGL score of 10.77 ± 1.58.
  • Multiple readability indices confirmed the complex nature of the generated text.

Conclusions:

  • AI tools like ChatGPT-4o can serve as dependable sources for pediatric asthma information.
  • Readability challenges in AI-generated content may impede its practical use in clinical settings for asthma management.
  • Further research is needed to optimize AI-generated medical content for better patient understanding.