Prompt Engineering in Large Language Models for Patient Education: A Systematic Review
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Background
Large language models (LLMs) have shown promise in generating patient-friendly medical content, but their outputs often vary in accuracy, readability, and relevance. Prompt engineering—structuring inputs to guide LLM responses—may improve the quality of educational materials, yet its impact on patient education remains unclear.
Objectives
To systematically review whether prompt engineering improves readability, accuracy, and usability of LLM-generated content for patient education.
Methods
We conducted a systematic review in accordance with PRISMA guidelines. PubMed, Scopus, and Web of Science were searched for original studies evaluating prompt engineering techniques in patient education. Data were extracted on prompt types, LLM models used, and outcomes. Risk of bias was assessed using the QUADAS-2 tool, and a narrative synthesis was performed.
Results
Our search identified five studies that met our criteria, focusing on answering patient questions and generating medical information. Prompt engineering techniques included instruction-based, elaborated, role-defining, scene-defining, and domain-specific prompts. Structured prompting improved accuracy and comprehensiveness in several cases, particularly when specific formats or custom instructions were used. Readability gains were notable when prompts explicitly requested simpler language and reading levels, though some strategies unintentionally increased complexity. Variability in effectiveness across LLMs and prompt types was observed.
Conclusion
Prompt engineering can enhance the clarity and, in some cases, the accuracy of LLM-generated patient education materials. However, benefits vary by model and strategy. Standardized approaches and further research are needed to optimize prompts, minimize bias, and support reliable, accessible patient communication.
