Benchmarking readability, reliability, and scientific quality of large language models in communicating organoid science.

ABSTRACT:

Background

Organoids have become central platforms in precision oncology and translational research, increasing the need for communication that is accurate, transparent, and clinically responsible. Large language models (LLMs) are now widely consulted for organoid-related explanations, but their ability to balance readability, scientific rigor, and educational suitability has not been systematically established.

Methods

Five mainstream LLMs (GPT-5, DeepSeek, Doubao, Tongyi Qianwen, and Wenxin Yiyan) were systematically evaluated using a curated set of thirty representative organoid-related questions. For each model, twenty outputs were independently scored using the C-PEMAT-P scale, the Global Quality Score (GQS), and seven validated readability indices. Between-model differ

SUBMITTER: Sun M 

PROVIDER: S-EPMC12855483 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature