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Large Language Models Using Clinical Text in Pediatrics: A Scoping Review.

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Large language models (LLMs) show promise in pediatric care, but current research has gaps. Future studies need better data and clinician involvement for safe implementation of these AI tools.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Pediatric Healthcare Informatics
  • Clinical Natural Language Processing

Background:

  • Large language models (LLMs) are increasingly used for clinical text analysis, with growing interest in healthcare integration.
  • However, their application and evaluation within pediatric care settings remain significantly underexplored.

Purpose of the Study:

  • To systematically map the existing literature on LLM applications in pediatrics using clinical text.
  • To identify critical evidence gaps and outline future directions for implementation and rigorous evaluation.

Main Methods:

  • A comprehensive search of major databases (PubMed, Embase, Web of Science, Scopus) and preprint servers was conducted for original research from January 2020 to July 2025.
  • Studies utilizing transformer-based LLMs with pediatric clinical text were included, with data extraction and synthesis performed by independent reviewers.
  • Adherence to Minimum Information for Medical AI Reporting (MINIMAR) standards was assessed.

Main Results:

  • The review included 40 studies (2023-2025), primarily retrospective observational studies from the US, focusing on diagnostic support and treatment planning.
  • Early childhood populations (0-5 years) were underrepresented, and most studies lacked pediatric-specific data fine-tuning and did not meet MINIMAR standards.
  • Significant gaps were found in reporting clinician involvement and interannotator agreement for model performance evaluation.

Conclusions:

  • LLMs offer potential for pediatric diagnostic support and treatment planning, but current evidence highlights limitations in scientific rigor.
  • Insufficient use of pediatric-specific data and inadequate reporting standards may impede safe and effective clinical implementation.
  • Future research must prioritize standardized reporting, clinician engagement, and broader age group representation.