Same child, different risk: demographic bias in childhood obesity attribution by large language models.
Large language models attributed higher obesity risk to low-income children in most comparisons
Same child, different risk: demographic bias in childhood obesity attribution by large language models.
Large language models (large language models) are increasingly consulted for pediatric health information, yet their demographic biases remain unsystematically evaluated in pediatric contexts.
To assess bias and variability in childhood obesity risk attribution across seven large language models (ChatGPT, Claude, DeepSeek, Gemini, GLM, Grok, and Qwen), spanning both Western and Chinese-origin developers; all prompts, including those submitted to the Chinese-origin models, were in English only.
A structured prompt-based experimental design was employed across six clinical domains (general obesity risk, dietary pattern, physical activity, sleep, mental health, and genetic predisposition) and six demographic comparison dimensions (sex, three race/ethnicity pairings, socioeconomic status, and urban-rural residence).
Claude achieved the highest neutral prompt composite score (mean 3.00 ± 0.91) and GLM the lowest (1.44 ± 0.51); between-model differences were statistically significant (Kruskal-Wallis H = 46.21, p < 0.001).
All models achieved a 100% Stigmatizing/Harmful Language pass rate, yet representation and cultural fit were universally weak.
Most models attributed higher obesity risk to Black and Hispanic/Latino children across the majority of domains.
Urban-rural attribution showed the greatest cross-model directional inconsistency (decision change rate 52.4%), with Western-origin models favoring rural attribution and Chinese-origin models favoring urban attribution.
Publicly accessible English-language web-interface outputs from current large language models showed systematic demographic patterns in pediatric obesity risk attribution, supporting the need for pre-deployment and post-deployment bias auditing before clinical or consumer health use.