The Role of Generative Artificial Intelligence in Pediatric Healthcare Professions Education: A Narrative Review

Abstract

The advent of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), presents transformative potential for pediatric healthcare professions education. This narrative review explores the current applications, educational opportunities, and inherent risks associated with integrating GenAI into the training of future pediatric healthcare professionals, including nurses, medical imaging technicians, and physiotherapists. Current applications range from informal student use for study synthesis and query resolution to structured curriculum implementations involving simulated clinical scenarios and AI-assisted learning. LLMs offer significant educational opportunities by enabling personalized learning, providing immediate access to information, simulating complex or rare cases, and reducing linguistic barriers. However, critical risks such as informational hallucinations, algorithmic biases, cognitive dependency, and ethical concerns necessitate a cautious approach. The review highlights the crucial need for a robust, integrated, and supervised educational model. This model should emphasize AI literacy, structured integration of GenAI tools with low-risk applications initially, followed by more complex simulations, ethical decision-making modules, and collaborative curriculum co-design. The essential role of faculty training and ongoing systematic evaluation of effectiveness is underscored. Ultimately, the successful and safe integration of GenAI in pediatric healthcare education hinges on a balanced approach that amplifies humanistic qualities of care, such as empathy and critical reasoning, rather than replacing them, ensuring the formation of competent and ethically grounded professionals prepared for the future of pediatric healthcare.