Potential to perpetuate social biases in health care by Chinese large language models: a model evaluation study

作者：Chenxi Liu, Chenxi Liu, Jianing Zheng, Yushu Liu, Xi Wang, Yuting Zhang, Wenwen Yu, Ting Yu, Ting Yu, Dan Wang, Dan Wang, Chaojie Liu, Chaojie Liu · 发表于：International Journal for Equity in Health · 年份：2025 · DOI：10.1186/s12939-025-02581-5 · 被引用次数：5 · 研究领域：Healthcare Systems and Reforms、Advanced Causal Inference Techniques、Machine Learning in Healthcare

BACKGROUND: Large language models (LLMs) may perpetuate or amplify social biases toward patients. We systematically assessed potential biases of three popular Chinese LLMs in clinical application scenarios. METHODS: We tested whether Qwen, Erine, and Baichuan encode social biases for patients of different sex, ethnicity, educational attainment, income level, and health insurance status. First, we prompted LLMs to generate clinical cases for medical education (n = 8,289) and compared the distribution of patient characteristics in LLM-generated cases with national distributions in China. Second, New England Journal of Medicine Healer clinical vignettes were used to prompt LLMs to generate differential diagnoses and treatment plans (n = 45,600), with variations analyzed based on sociodemographic characteristics. Third, we prompted LLMs to assess patient needs (n = 51,039) based on clinical cases, revealing any implicit biases toward patients with different characteristics. RESULTS: The three LLMs showed social biases toward patients with different characteristics to varying degrees in medical education, diagnostic and treatment recommendation, and patient needs assessment. These biases were more frequent in relation to sex, ethnicity, income level, and health insurance status, compared to educational attainment. Overall, the three LLMs failed to appropriately model the sociodemographic diversity of medical conditions, consistently over-representing male, high-education and high-...