小児外傷性脳損傷の症例記述の家族伝達のための人工知能駆動型適応

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.

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.

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.

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