Parenting Journal · Volume 1 · Issue 2 · Licensed source edition
Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study
Originally published 2026-06-18 · JMIR Medical Informatics
JMIR Publications Inc. · DOI 10.2196/93054 ↗
AI / system type
Generative AI / reliability-and-readability benchmark
Family implicationsDesign, ethics, stakeholder or technical evidence with child/family implications; household benefits are not assumed.
The AI connection
Compares five LLM services answering parent-oriented pediatric anesthesia questions, including safety and literacy metrics.
Why this reading matters
Shows why information design and source transparency matter even when average accuracy is rated favorably.
Editorial reading guide · Trust
What makes AI-generated parent information understandable as well as technically accurate?
This benchmark brings reliability, transparency and reading complexity into the same evaluation, rather than treating correctness alone as sufficient.
Study context
Thirty-three parent-facing questions evaluated across five LLMs by two specialists
Age / stage: Pediatric anesthesia education; no parent or child sample
Read with three questions
- How did expert accuracy, transparency and readability results differ?
- What hallucinations or unsafe statements were identified?
- Which outcomes would need to be tested with actual caregivers?
Keep the limits in view
The evaluation was prompt- and date-specific with expert-selected questions and no user or clinical trial. Readability formulas do not establish comprehension, and model quality changes over time.
The text below is the original article’s licensed repository edition. Its wording is not rewritten. Use the Paper Agent beside the text to locate evidence; open the source for figures, tables and complete context.
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Follow the original.
Original authorship, publication date, journal and DOI are preserved. This issue’s TRICK placement and reading questions are editorial additions, not claims made by the original authors.
Fulin Pu; Jishuang Hong; Xiaoying Wei; Yanling Chen; Andrew Coristine (2026). Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study JMIR Medical Informatics. https://doi.org/10.2196/93054
Permissions checked against the saved article notice; repository publication notices screened 2026-11-04. This is not a new peer-review decision. The source authors and publishers are not represented as endorsing this issue.