A review protocol examining the application of large language models in healthcare education and public health learning spaces

ObjectiveThrough this scoping review, we aim to explore and synthesize existing knowledge and evidence on the learning approaches for incorporating LLMs into healthcare education and public health learning spaces. Specifically, we will attempt to investigate methods for auditing prompts for accuracy; tailoring prompts to improve task-specific accuracy and utility; and exploring how end-user feedback is used to refine and optimize LLM prompts over time. This review will provide a comprehensive understanding of how LLMs are being tailored and improved in these fields, contributing to the development of evidence-based strategies for their implementation. It will also identify areas for future research and innovation. IntroductionThe increasing integration of large language models (LLMs) into healthcare and public health practice and research highlights their potential to revolutionize service delivery, decision-making, and patient care. Despite these advancements, understanding how LLMs can be effectively tailored, audited, and refined for healthcare-specific tasks remains a critical area of inquiry. Key issues include, the accuracy of generated information, and their relevance to the medical and public health fields. Inclusion criteriaInclusion criteria will focus on studies addressing LLM applications in healthcare and public health, prompt engineering techniques, prompt auditing methods, and processes geared towards integrating user feedback. Articles that do not focus on healthcare or public health contexts and lack relevance to LLM learning approaches will be excluded. MethodsThe review is guided by the JBI methodology for scoping reviews complemented by updates from Levac et al. Databases including PubMed, Scopus, IEEE Xplore, and Web of Science will be searched for peer-reviewed articles, conference proceedings, and grey literature published in English and French from 2015 to 2025. Data extraction will include information on study characteristics, LLM models, prompt engineering strategies, auditing methodologies, and user feedback mechanisms. We will synthesize to identify trends, gaps, and best practices in leveraging LLMs to generate baseline data for auditing prompts that optimize AI learning and education needs in the healthcare and public health sector.