Evaluating large language models for simplifying non-English medical consent with clinician involvement

Informed consent forms are essential for protecting patient rights, but are often difficult to understand, especially in non-English settings where formal language and long, context-dependent sentences hinder comprehension. This study evaluated whether large language models (LLMs) can simplify Chinese language surgical consent forms and whether clinician revision can further improve the output. Official forms from nine hospitals were used to create three versions of each document: the original, an LLM-simplified version, and a clinician-revised version. We assessed text structure, readability, content quality, and layperson comprehension using quantitative metrics and expert ratings. The LLM version improved readability and comprehension but reduced content quality, particularly risk information. Clinician revision restored accuracy while maintaining clarity and achieved the highest comprehension scores. Linear mixed effects modeling confirmed these trends. These findings highlight both the impact of LLM-based simplification and the value of human AI collaboration in patient communication.