Large Language Models in Pediatric Education: Current Uses and Future Potential

Area of Science:

• Medical Education
• Artificial Intelligence
• Pediatrics

Background:

• Generative artificial intelligence (AI), especially large language models (LLMs), presents significant opportunities and challenges in pediatric education.
• LLMs can assist with curriculum development, individualized trainee support, and enhancing clinical practice for pediatricians.

Purpose of the Study:

• To explore the history, current applications, and challenges of generative AI in pediatric education.
• To provide examples of LLM capabilities and discuss future directions for responsible integration.

Main Methods:

• Review of current literature and applications of LLMs in medical education, specifically pediatrics.
• Analysis of potential benefits, risks, and ethical considerations associated with LLM use.

Main Results:

• LLMs can enhance curriculum design, create personalized learning plans, improve information retrieval, and refine patient education materials.
• Current LLMs may produce inaccuracies ('hallucinations') and raise ethical concerns regarding bias and plagiarism.

Conclusions:

• The judicious use of LLMs by content experts can leverage their benefits in pediatric education while mitigating risks.
• Establishing clear guidelines and policies is crucial for the safe and effective adoption of AI in child healthcare education.