Prompt engineering for accurate statistical reasoning with large language models in medical research

Prompt design is a critical determinant of output quality in AI-assisted statistical analysis. Hybrid prompting strategies should be adopted as best practice in medical research to ensure methodological rigor and reproducibility. Additional testing with newer models, including Claude 4 Sonnet, Claude 4 Opus, o3 mini, and o4 mini, confirmed the consistency of results, supporting the generalizability of findings across both Anthropic and OpenAI model families. This study highlights prompt engineering as a core competency in AI-assisted medical research and calls for the development of standardized prompt templates, evaluation rubrics, and further studies across diverse statistical domains to support robust and reproducible scientific inquiry.