Large language model chatbots as sources of pediatric anesthesia health advice: An evaluation of reliability and readability
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By
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Xue Zhang
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Yuchen Dai
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Xin Zhao
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Lin Wu
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Boming Shao
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Xisheng Shan
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Fuhai Ji
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Runzhi Deng
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Baojian Zhao
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June 29, 2026
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Clinical Scorecard: Assessing the Reliability and Readability of Pediatric Anesthesia Guidance from Large Language Model Chatbots
At a Glance
| Category | Detail |
|---|
| Condition | Pediatric Anesthesia |
| Key Mechanisms | Use of large language models (LLMs) for generating health-related information. |
| Target Population | Caregivers of neonates, infants, children, and adolescents undergoing anesthesia. |
| Care Setting | Digital health communication and perioperative education. |
Key Highlights
- High prevalence of perioperative anxiety in pediatric patients (65% to 80%).
- Parents increasingly seek online health information prior to hospital visits.
- Quality of online health content varies significantly.
- LLMs may provide inaccurate or overly complex information.
- Study evaluates four LLMs for reliability and readability in pediatric anesthesia.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
- Inaccurate information may lead to increased caregiver anxiety and adverse postoperative outcomes.
Patient & Prescribing Data
Children undergoing surgical procedures requiring anesthesia.
Caregivers require clear and accurate information to support perioperative preparation.
Clinical Best Practices
- Ensure accuracy and clarity in health information provided to caregivers.
- Utilize standardized assessment frameworks for evaluating digital health content.
Related Resources & Content
