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The Role of Large Language Models in Identifying and Correcting Paediatric Health Misinformation for Parents: A
1Selcuk University Akşehir Kadir Yallagöz Health School, Akşehir, Konya, Turkey.
Insights
Large language models (LLMs) show potential for providing safe, guideline-adherent paediatric health information online. However, expert oversight remains crucial to ensure accuracy and prevent misinformation for parents seeking child health guidance.
Area of Science:
- Digital Health
- Artificial Intelligence in Healthcare
- Paediatric Nursing
Background:
- Increasing parental reliance on digital platforms for child health information.
- Growing risk of exposure to health misinformation online.
- Need for reliable, evidence-based information sources for parents.
Purpose of the Study:
- Evaluate large language models' (LLMs) performance in addressing paediatric misinformation.
- Assess LLMs' ability to identify, correct, and generate guideline-adherent responses.
- Determine the safety and accuracy of LLM-generated health information for children.
Main Methods:
- Compilation of 20 common paediatric misinformation statements.
- Presentation of statements to four LLMs (ChatGPT, Gemini Advanced, Claude, Microsoft Copilot).
- Evaluation of LLM responses by medical professionals for accuracy, guideline adherence, explanation quality, and risk.
Main Results:
- ChatGPT and Gemini Advanced exhibited the highest accuracy and guideline adherence.
- Claude and Microsoft Copilot showed deficiencies in explanation quality and guideline adherence, respectively.
- Low risk scores and no hazardous content detected across all evaluated LLMs.
Conclusions:
- LLMs can conditionally provide safe, guideline-aligned paediatric information.
- Professional oversight and adherence to evidence-based guidelines are essential for LLM use.
- Highlights LLMs' potential applications and limitations in digital health safety and paediatric nursing.
Background:
As parents increasingly seek information about child health on digital platforms, the risk of misinformation has also risen.
Aims:
This study aimed to evaluate the performance of large language models (LLMs) in identifying, correcting and generating guideline-adherent, evidence-based responses to paediatric misinformation.
Method:
Twenty common paediatric misinformation statements across nine thematic areas were identified based on literature review and expert opinion. These statements were presented to four different LLMs (ChatGPT, Gemini Advanced, Claude, Microsoft Copilot) with three repetitions each. The generated responses were evaluated by one physician and two nurses in terms of accuracy, guideline adherence, explanation quality and risk.
Results:
ChatGPT and Gemini demonstrated the highest and most consistent performance in accuracy and guideline adherence. Claude showed some deficiencies in certain explanations, while Copilot exhibited lower performance in guideline adherence and explanation depth compared to the other models. Risk scores were low across all models, and no hazardous content was observed. Furthermore, the models' abilities to correct misinformation varied in terms of guideline-compliant explanations and risk communication.
Conclusion:
Within the scope of the evaluated paediatric misinformation statements and current model versions, LLMs may provide conditionally safe and guideline-aligned information for parents. However, professional oversight and adherence to evidence-based paediatric guidelines remain essential. This study systematically highlighted the potential applications and limitations of LLMs in digital health safety and paediatric nursing practice.
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