Feasibility of Large Language Models Assisting in the Creation of Health Science Popularization Works

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ObjectiveTo explore the feasibility of using Large Language Models (LLMs) to generate evidence-based health science popularization materials by surveying public needs, designing specific prompts, and evaluating the quality of the generated content.MethodsAn online survey was conducted via SoJump platform to investigate public needs for healthy lifestyle information. The Kimi LLM was employed to generate health science popularization materials, with prompts optimized based on literature review and expert feedback. Two health educators independently evaluated the generated materials using the DISCERN instrument. Data were analyzed using SPSS 24.0 software.ResultsThe primary barriers hindering residents from accessing and using health information were distrust of publishing institutions (71.37%), excessive use of technical terminology (64.71%), and inaccessible channels (60.00%). The designed prompts, consisting of system and user prompts, comprised three sections: role definition, content generation, and structure/output formatting specifications. These prompts effectively guided the LLM to produce accessible health science materials with a standardized structure, including a title, introduction, specific interventions, and summary. Following prompt optimization, the DISCERN scores of the generated materials increased by (4.40±2.51) points, representing a statistically significant difference (P=0.041).ConclusionsHealth science popularization is a crucial factor in promoting the acquisition and utilization of health information among residents. Developing prompts to assist LLMs in creating health science materials holds significant potential; it facilitates health education work and enhances both the scientific accuracy and efficiency of content production.

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