Artificial intelligence for tailoring complex clinical information to patients and families: A technical perspective

Effective communication of complex medical information is critical for individuals with spinal cord injury (SCI) and their families, but this need remains largely unmet. Artificial intelligence (AI), including large language models (LLMs) like GPT-4o, may help simplify clinical texts while preserving essential medical information. However, their ability to adapt content across ages, education levels, and languages without compromising accuracy has not been systematically evaluated. We analyzed short excerpts (∼100 words) from de-identified Spanish-language SCI clinical reports that had historically posed communication challenges. A bilingual co-author produced human English translations, creating parallel English and Spanish corpora (10 texts per language: 4 originals and 6 audience-tailored simplifications). GPT-4o was applied for within-language simplification, not translation. Baseline readability quantifies the complexity of real-world clinical documentation directly. All original English texts had very high complexity by Flesch-Kincaid Grade Level (FKGL >18), whereas simplified texts reached FKGL 7.3 for children and 9.8–10.6 for adolescents and adults. Experts rated child-directed fidelity 2–3/5; adult-directed texts scored 4–5/5 for fidelity and appropriateness. Original Spanish texts ranged from “Difficult” to “Very Difficult” by the Fernández–Huerta (FH) index, showing greater variability than English. Readability classifications were not concordant across languages (3/10 cases, 30%); e.g. one simplified version was “Plain English” by FKGL but “Fairly Difficult” by FH, highlighting language-specific behavior. GPT-4o can tailor complex SCI clinical excerpts to specific audiences in English and Spanish, but child-directed versions may lose clinically relevant information. Clinician oversight remains essential for safe patient communication.