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Artificial Intelligence-Driven Adaptation of Pediatric Traumatic Brain Injury Case Descriptions for Family

Alejandro García-Rudolph^1,2,3, Marc Navarro Berenguel^1,2,3, Eloy Opisso^1,2,3

¹Departmento de Investigación e Innovación, Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain.

Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists

|February 5, 2026

Abstract

Objective:

We examined whether GPT-4o, a widely used large language model (LLM), could produce age- and education-appropriate versions of complex pediatric traumatic brain injury case descriptions, while preserving clinical accuracy and emotional tone.

Methods:

Five cases were adapted into four audience scenarios. Text complexity was assessed via Flesch-Kincaid (FKS), Gunning Fog, and SMOG indices. Clinical human experts rated text fidelity and emotional appropriateness on a 3-point scale.

Results:

Original texts showed very high complexity (FKS 18.2-20.5), equivalent to 18-20 years of education. Adaptations for parents with high school education were often over-simplified (FKS 4.75-7.1), while versions for 12-year-olds were well-matched (FKS ~5-6). Texts for 8-year-olds had FKS scores of 4.0-6.8 (above grade 2-3 targets) and reduced fidelity (scores 1-2). Emotional tone was consistently rated appropriate across all audiences.

Conclusion:

Clinicians may use LLMs to draft explanations, but must carefully review and tailor them.

Keywords:

Flesch–Kincaid GPT Large language models Pediatric Readability Traumatic brain injury

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