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Designing Patient-Centered Communication Aids in Pediatric Surgery Using Large Language Models.

Arya S Rao1, Aneesh Mazumder2, Elizabeth Roux1

  • 1Harvard Medical School, Boston, MA, United States; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center, Mass General Brigham, Boston, MA, United States.

Journal of Pediatric Surgery
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise in explaining pediatric surgeries to children. GPT-4-turbo generally outperformed Gemini 1.0 Pro in accuracy and age-appropriateness, though both models require further refinement for equitable patient communication.

Keywords:
Age-appropriate explanationsArtificial intelligenceDigital health innovationLarge language models (LLMs)Patient-centered communicationPediatric communicationPediatric surgery

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing for Healthcare Communication
  • Pediatric Surgical Patient Education

Background:

  • Communicating complex pediatric surgical information to patients of varying ages presents significant challenges.
  • Large language models (LLMs) offer a potential solution for translating specialized medical information into accessible language.
  • This study investigates the efficacy of LLMs in generating age-appropriate explanations for common pediatric surgeries.

Purpose of the Study:

  • To evaluate the performance of two leading LLMs (GPT-4-turbo and Gemini 1.0 Pro) in generating explanations of pediatric surgeries.
  • To assess the accuracy, completeness, age-appropriateness, and potential demographic bias of LLM-generated content.
  • To determine the overall quality and utility of LLMs as patient-centered communication aids in pediatric surgery.

Main Methods:

  • Two generalist LLMs were prompted to explain four common pediatric surgeries to children of various ages and genders.
  • Responses were rated by pediatricians and general surgeons on accuracy, completeness, age-appropriateness, and demographic bias using a five-point Likert scale.
  • Statistical analysis, including ordinal mixed-effects modeling, was employed to evaluate the collected ratings.

Main Results:

  • Both LLMs produced explanations with moderately high overall quality, but GPT-4-turbo generally received higher ratings across all measures.
  • GPT-4-turbo responses were rated as highly accurate, complete, and age-appropriate, with performance improving with patient age.
  • Gemini 1.0 Pro responses were moderately accurate and age-appropriate, with performance tending to decrease as patient age increased; slight gender-based differences were noted.

Conclusions:

  • Off-the-shelf LLMs demonstrate potential for generating accurate, complete, and age-appropriate pediatric surgery explanations with low bias.
  • Significant inter-model variability exists, highlighting the need for further validation and clinical content fine-tuning.
  • LLMs can be valuable tools for enhancing patient education and ensuring equitable communication of medical information at the point of care.